diff --git a/BOLSIGChemistry_NominalRates/3BdyRecomb_4p.h5 b/BOLSIGChemistry_NominalRates/3BdyRecomb_4p.h5
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index 000000000..38741b7ef
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diff --git a/BOLSIGChemistry_NominalRates/3BdyRecomb_Ground.h5 b/BOLSIGChemistry_NominalRates/3BdyRecomb_Ground.h5
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index 000000000..a92f093ce
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diff --git a/BOLSIGChemistry_NominalRates/3BdyRecomb_Metastable.h5 b/BOLSIGChemistry_NominalRates/3BdyRecomb_Metastable.h5
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index 000000000..7b3192331
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diff --git a/BOLSIGChemistry_NominalRates/3BdyRecomb_Resonant.h5 b/BOLSIGChemistry_NominalRates/3BdyRecomb_Resonant.h5
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index 000000000..fcc11aabf
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diff --git a/BOLSIGChemistry_NominalRates/DeExcitation_4p.h5 b/BOLSIGChemistry_NominalRates/DeExcitation_4p.h5
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index 000000000..54e6ace87
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diff --git a/BOLSIGChemistry_NominalRates/DeExcitation_Metastable.h5 b/BOLSIGChemistry_NominalRates/DeExcitation_Metastable.h5
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index 000000000..e436c6660
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diff --git a/BOLSIGChemistry_NominalRates/DeExcitation_Resonant.h5 b/BOLSIGChemistry_NominalRates/DeExcitation_Resonant.h5
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index 000000000..bcf78ca74
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diff --git a/BOLSIGChemistry_NominalRates/Excitation_4p.h5 b/BOLSIGChemistry_NominalRates/Excitation_4p.h5
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index 000000000..5e1358d5c
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diff --git a/BOLSIGChemistry_NominalRates/Excitation_Metastable.h5 b/BOLSIGChemistry_NominalRates/Excitation_Metastable.h5
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index 000000000..3cfec52c6
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diff --git a/BOLSIGChemistry_NominalRates/Excitation_Resonant.h5 b/BOLSIGChemistry_NominalRates/Excitation_Resonant.h5
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index 000000000..f4b6fff0d
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diff --git a/BOLSIGChemistry_NominalRates/Ionization.h5 b/BOLSIGChemistry_NominalRates/Ionization.h5
new file mode 100644
index 000000000..b0cb3e61d
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diff --git a/BOLSIGChemistry_NominalRates/StepExcitation.h5 b/BOLSIGChemistry_NominalRates/StepExcitation.h5
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index 000000000..f24fecab9
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diff --git a/BOLSIGChemistry_NominalRates/StepIonization_4p.h5 b/BOLSIGChemistry_NominalRates/StepIonization_4p.h5
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index 000000000..c9ae36f79
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diff --git a/BOLSIGChemistry_NominalRates/StepIonization_Metastable.h5 b/BOLSIGChemistry_NominalRates/StepIonization_Metastable.h5
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index 000000000..9cd0f8f0d
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diff --git a/BOLSIGChemistry_NominalRates/StepIonization_Resonant.h5 b/BOLSIGChemistry_NominalRates/StepIonization_Resonant.h5
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index 000000000..4014ea28b
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diff --git a/BOLSIGChemistry_NominalRates/detailed_balance.py b/BOLSIGChemistry_NominalRates/detailed_balance.py
new file mode 100644
index 000000000..24f58fc64
--- /dev/null
+++ b/BOLSIGChemistry_NominalRates/detailed_balance.py
@@ -0,0 +1,135 @@
+import numpy as np
+import h5py as h5
+from matplotlib import pyplot as plt
+
+E_lvl_m = 11.548
+E_lvl_r = 11.624
+E_lvl_4p = 12.907
+E_lvl_i = 15.76
+g_m = 5.0
+g_r = 3.0
+g_4p = 3.0
+g_i = 4.0
+n_e = 3.7e15
+
+rates14 = []
+rates21 = []
+rates26 = []
+rates29 = []
+rates35 = []
+data10 = h5.File('./1s-metastable.h5', 'r')["table"]
+data11 = h5.File('./1s-resonance.h5', 'r')["table"]
+data12 = h5.File('./2p-lumped.h5', 'r')["table"]
+data13 = h5.File('./Ionization.h5', 'r')["table"]
+data15 = h5.File('./StepIonization.h5', 'r')["table"]
+
+Te = data10[:,0]
+rates10 = data10[:,1]
+rates11 = data11[:,1]
+rates12 = data12[:,1]
+rates13 = data13[:,1]
+rates15 = data15[:,1]
+
+for j in range(len(Te)):
+    rates14.append(rates10[j] * (1 / g_m) * np.exp(E_lvl_m / (Te[j] / 11604)))
+    rates21.append(rates11[j] * (1 / g_r) * np.exp(E_lvl_r / (Te[j] / 11604)))
+    rates26.append(rates15[j] * (g_m / g_i) * np.exp((E_lvl_i - E_lvl_m) / (Te[j] / 11604)) / n_e * 6.022e23)
+    rates29.append(rates13[j] * (1 / g_i) * np.exp(E_lvl_i / (Te[j] / 11604)) / n_e * 6.022e23)
+    rates35.append(rates12[j] * (1 / g_4p) * np.exp(E_lvl_4p / (Te[j] / 11604)))
+
+
+data14 = np.empty(shape = (len(Te), 2))
+data21 = np.empty(shape = (len(Te), 2))
+data26 = np.empty(shape = (len(Te), 2))
+data29 = np.empty(shape = (len(Te), 2))
+data35 = np.empty(shape = (len(Te), 2))
+
+data14[:,0] = Te
+data14[:,1] = rates14
+data21[:,0] = Te
+data21[:,1] = rates21
+data26[:,0] = Te
+data26[:,1] = rates26
+data29[:,0] = Te
+data29[:,1] = rates29
+data35[:,0] = Te
+data35[:,1] = rates35
+
+with h5.File('./Deexci-metastable.h5', 'w') as f:
+    dataset = f.create_dataset("table", data = data14)
+
+with h5.File('./Deexci-resonance.h5', 'w') as g:
+    dataset = g.create_dataset("table", data = data21)
+
+with h5.File('./3BdyRecomb-ground.h5', 'w') as h:
+    dataset = h.create_dataset("table", data = data29)
+
+with h5.File('./3BdyRecomb-metastable.h5', 'w') as x:
+    dataset = x.create_dataset("table", data = data26)
+
+with h5.File('./Deexci-2p.h5', 'w') as y:
+    dataset = y.create_dataset("table", data = data35)
+
+#rxn10 = h5.File("./BOLSIGChemistry_6SpeciesRates/lumped.metastable.h5", 'r')
+#rxn11 = h5.File("./BOLSIGChemistry_6SpeciesRates/lumped.resonance.h5", 'r')
+
+#data10 = rxn10["table"]
+#data11 = rxn11["table"]
+
+#Te = data10[:,0]
+#Te /= 11604
+#rates10 = data10[:,1]
+#rates11 = data11[:,1]
+#rates10 /= 6.022e23
+#rates11 /= 6.022e23
+
+#rates14 = []
+#rates21 = []
+
+#for i in range(len(rates10)):
+#    rates14.append(rates10[i] * (1 / g_m) * np.exp(E_lvl_m / Te[i]))
+#    rates21.append(rates11[i] * (1 / g_r) * np.exp(E_lvl_r / Te[i]))
+
+
+#arrh_14 = []
+#arrh_21 = []
+
+#for j in range(len(Te)):
+#    arrh_14.append(4.3e-16 * Te[j]**0.74)
+#    arrh_21.append(4.3e-16 * Te[j]**0.74)
+
+
+#fig,ax = plt.subplots()
+#ax.set_xscale('log')
+#ax.set_yscale('log')
+#ax.plot(Te, rates14, color = 'darkblue', label = 'CRSC - 14')
+#ax.plot(Te, arrh_14, color = 'cyan', label = 'Arrhenius - 14')
+#ax.plot(Te, rates21, color = 'darkred', label = 'CRSC - 21')
+#ax.plot(Te, arrh_21, color = 'orange', label = 'Arrhenius - 21')
+#ax.legend()
+#ax.set_xlabel('Te [eV]')
+#ax.set_ylabel('Rate [# / m3-s]')
+#plt.savefig('RatesComparison.png')
+
+
+#for i in range(len(rates14)):
+#    rates14[i] *= 6.022e23
+#    rates21[i] *= 6.022e23
+#    Te[i] *= 11604
+
+#data14 = np.empty(shape = (len(Te), 2))
+#data21 = np.empty(shape = (len(Te), 2))
+
+#data14[:,0] = Te
+#data21[:,0] = Te
+#data14[:,1] = rates14
+#data21[:,1] = rates21
+
+#with h5.File('./BOLSIGChemistry_6SpeciesRates/deexci.metastable.h5', 'w') as f:
+#    dataset = f.create_dataset("table", data = data14)
+
+#with h5.File('./BOLSIGChemistry_6SpeciesRates/deexci.resonance.h5', 'w') as g:
+#    dataset = g.create_dataset("table", data = data21)
+
+
+
diff --git a/BOLSIGChemistry_NominalRates/nominal_transport.h5 b/BOLSIGChemistry_NominalRates/nominal_transport.h5
new file mode 100644
index 000000000..47b16dc12
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diff --git a/BOLSIGChemistry_NominalRates/plot_rates.py b/BOLSIGChemistry_NominalRates/plot_rates.py
new file mode 100644
index 000000000..355e3cde0
--- /dev/null
+++ b/BOLSIGChemistry_NominalRates/plot_rates.py
@@ -0,0 +1,157 @@
+import numpy as np
+import csv
+from matplotlib import pyplot as plt
+from scipy.interpolate import CubicSpline
+import matplotlib.colors as mcolors
+
+tau = (1./13.56e6)
+nAr = 3.22e22
+np0 = 8e16
+Nr = 9
+iSample = 0
+
+reactionExpressionTypelist =  np.array([True,True,True,False,False,False,False,False,False])
+
+reactionsList = []
+	# import h5py as h5
+	# rxnName = ["Ionization", ...] <- dictionary containing reaction name root string
+	# for r in range(Nr):
+	#	if reactionExpressionTypeList[r]:
+	#		fileName = "{0:s}.{1:08d}.h5".format(rxnName[r], iSample)
+	#		f = h5.File(filename, "r")
+	#		D = f["table"]
+
+for i in range(Nr):
+        if reactionExpressionTypelist[i]:
+            Nsample = 1
+            N300 = 200
+
+            root_dir = "./"
+            rate_file = open("{0:s}reaction300K_{1:d}.txt".format(root_dir, i), 'r', encoding='utf-8-sig')
+            temp_file = open('reaction300K_Te.txt', 'r', encoding='utf-8-sig')
+
+            rateCoeff = np.genfromtxt(rate_file, dtype='float')
+            rateCoeff = np.reshape(rateCoeff,[Nsample, N300]).T[:,iSample]
+            rate_file.close()
+
+            Te = np.genfromtxt(temp_file, dtype='float')
+            print(Te)
+            Te = np.reshape(Te,[Nsample, N300]).T[:,iSample]
+            print(Te)
+            temp_file.close()
+
+            # Sorting mean energy array and rate coefficient array based on
+            # the mean energy array.
+            Teinds = Te.argsort()
+            rateCoeff = rateCoeff[Teinds]
+            Te = Te[Teinds]
+
+            # Find duplicates
+            TeDuplicateinds = np.where(np.abs(np.diff(Te, axis=0)) > 0.0)
+            TeDuplicateindsForLog = np.where(np.abs(np.diff(Te, axis=0)) == 0.0)
+            rateCoeff = rateCoeff[TeDuplicateinds]
+            Te = Te[TeDuplicateinds]
+
+            # Nondimensionalization of mean energy.
+            # Te *= 1.5
+
+            # Find first non-zero value of the coefficient rate.
+            I = np.nonzero(rateCoeff)
+
+            diffRateCoeff = [j-i for i, j in zip(rateCoeff[:-1], rateCoeff[1:])]
+            diffTe = [j-i for i, j in zip(Te[:-1], Te[1:])]
+
+            Monotonicity = np.asarray([j/i for i, j in zip(diffTe, diffRateCoeff)])
+            Monotonicity = np.insert(Monotonicity, 0, 0.0, axis=0)
+
+            Nan = np.isnan(Monotonicity)
+            Inf = np.isinf(Monotonicity)
+            indexPositive = np.where(Monotonicity>0.0)
+            Positive = np.full(Monotonicity.shape, False, dtype=bool)
+            Positive[indexPositive] = True
+
+            indices = Nan + Inf + Positive
+
+            lastFalse = np.where(indices==False)[-1][-1] + 2
+
+            # Transformation to log scale.
+            TeLog = np.log(Te)
+
+            # Compute the slope of the rate coefficient between its first two non-zero values.
+            # Finite differences are used.
+            dydx = (rateCoeff[lastFalse + 1] - rateCoeff[lastFalse]) \
+                 / (Te[lastFalse + 1] - Te[lastFalse])
+
+            # Arrhenius form: kf = A * exp(-C / Te)
+            C = Te[lastFalse]**2.0*dydx / rateCoeff[lastFalse]
+
+            # Compute pre-exponential coefficient, A, in log scale.
+            ALog = np.log(rateCoeff[lastFalse]) + C / Te[lastFalse]
+
+            # Transform rate coefficient in log scale.
+            rateCoeffLog = np.zeros(rateCoeff.shape)
+            rateCoeffLog[lastFalse:] = np.log(rateCoeff[lastFalse:])
+            # For the troublesome values, we use the Arrhenius form.
+            rateCoeffLog[0:lastFalse] = ALog - C / Te[0:lastFalse]
+            # Nondimensionalization in log scale.
+            #if i < 2:
+            #    rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
+            #else:
+            #    rateCoeffLog += - np.log(1.0/tau) + np.log(np0)
+
+            # Interpolation in log scale.
+            reactionExpressionsLog = CubicSpline(TeLog, rateCoeffLog)
+            # Gradient in log scale
+            reactionTExpressionsLog = CubicSpline.derivative(reactionExpressionsLog)
+
+            #reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+            #                    rxnBolsig = reactionExpressionTypelist[i],
+            #                    kf_log = reactionExpressionsLog,
+            #                    kf_T_log = reactionTExpressionsLog)
+            #reactionsList.append(reaction)
+
+
+            # rxn   = eval("lambda energy :" + reactionExpressionslist[i])
+            # rxn_T = eval("lambda energy :" + reactionTExpressionslist[i])
+            # setting the axes at the centre
+            fig ,ax = plt.subplots(figsize=(9, 6))
+            ax.spines["top"].set_visible(True)
+            ax.spines["right"].set_visible(True)
+            ax.set_yscale('log')
+            ax.set_xscale('log')
+
+            # plot the function
+            #plt.plot(rateCoeffXFiner, np.exp(reactionExpressions_cubicSplineDerivative_log(rateCoeffXFiner)),
+            #   color='salmon', linestyle='--', label='interBolsig')
+            #plt.plot(rateCoeffXFine, np.exp(reactionExpressions_cubicSpline_log(rateCoeffXFine)),
+            #   color='lightgreen', linestyle='--', label='interBolsig')
+            #plt.plot(Te[:,0], reactionTExpressionsLogFiltered(TeLog[:]) * np.exp(reactionExpressionsLog(TeLog[:])) / Te[:,0],
+            #   color='blue', linestyle='-', label='interBolsig')
+            plt.plot(Te, np.exp(reactionExpressionsLog(TeLog[:])),
+               color='green', linestyle='-', label='Bolsig')
+            #plt.plot(Te, rxn(Te),
+            #   color='salmon', linestyle='--', label='Liu')
+            #plt.plot(Te[:,0], rxn_T(Te[:,0]),
+            #   color='red', linestyle='--', label='interBolsig')
+            plt.xlim((0.05,100))
+            plt.ylim((1e-50,300))
+            plt.legend()
+            plt.savefig("./PlotRates_%s.pdf" %str(i), dpi=300)
+            #plt.xlim((-0.0001,0.0255))
+            plt.show()
+
+            # Export rates to CSV Files
+            header = ['Te', 'kf']
+            row_temp = []
+            data = []
+            for j in range(len(Te)):
+                row_temp.append(Te[j])
+                row_temp.append(np.exp(reactionExpressionsLog(TeLog[j])))
+                data.append(row_temp)
+                row_temp = []
+            
+            f = open("./PlotRates_Rxn%s.csv" %str(i), 'w')
+            writer = csv.writer(f)
+            writer.writerow(header)
+            writer.writerows(data)
+            f.close()
diff --git a/BOLSIGChemistry_NominalRates/plot_rates_h5.py b/BOLSIGChemistry_NominalRates/plot_rates_h5.py
new file mode 100644
index 000000000..4716e11d7
--- /dev/null
+++ b/BOLSIGChemistry_NominalRates/plot_rates_h5.py
@@ -0,0 +1,110 @@
+import numpy as np
+import csv
+from matplotlib import pyplot as plt
+from scipy.interpolate import CubicSpline
+import matplotlib.colors as mcolors
+import h5py as h5
+
+Nr = 34
+
+reactionExpressionTypelist =  np.array([False,False,False,False,False,False,False,False, # Rxns 1-8
+                                        True,True,True,True,True,True,True,True,True,True,True,True,True,True, # Rxns 9-22
+                                        False,False,True,True,True,True,False,False,False,False,True,True]) # Rxns 23-34
+                                        
+rxnNameDict = { 0: "2Ar(m) => 2Ar",
+                1: "Ar(m) + Ar(r) => E + Ar+ + Ar",
+                2: "2Ar(4p) => E + Ar+ + Ar",
+                3: "2Ar(m) => E + Ar+ + Ar",
+                4: "Ar(m) + Ar => 2Ar",
+                5: "Ar(r) => Ar",
+                6: "Ar(4p) => Ar(m)",
+                7: "Ar(4p) => Ar(r)",
+                8: "1s-metastable",
+                9: "1s-resonance",
+               10: "2p-lumped",
+               11: "Ionization",
+               12: "Deexci-metastable",
+               13: "StepIonization",
+               14: "E + Ar(m) => E + Ar(r)",
+               15: "E + Ar(m) => E + Ar(4p)",
+               16: "StepIonization",
+               17: "E + Ar(4p) => E + Ar(r)",
+               18: "E + Ar(4p) => E + Ar(m)",
+               19: "Deexci-resonance",
+               20: "E + Ar(r) => E + Ar(m)",
+               21: "E + Ar(r) => E + Ar(4p)",
+               22: "E + Ar+ => Ar(m)",
+               23: "E + Ar+ => Ar(4p)",
+               24: "3BdyRecomb-metastable",
+               25: "3BdyRecomb-metastable",
+               26: "3BdyRecomb-metastable",
+               27: "3BdyRecomb-ground",
+               28: "Ar + Ar(4p) => Ar + Ar(m)",
+               29: "Ar + Ar(4p) => Ar + Ar(r)",
+               30: "Ar(m) + Ar(4p) => E + Ar+ + Ar",
+               31: "Ar(r) + Ar(4p) => E + Ar+ + Ar",
+               32: "StepIonization",
+               33: "Deexci-2p"}
+
+for i in range(Nr):
+        if reactionExpressionTypelist[i]:
+            if i < 14 or i == 16 or i == 19 or i > 23:
+                f = h5.File("{0:s}.h5".format(rxnNameDict[i]), 'r')
+                data = f["table"]
+            else:
+                f = h5.File("StepwiseExcitations.nominal.h5", 'r')
+                data = f[rxnNameDict[i]]
+
+            Te = data[:,0]
+            Te /= 11604
+            rateCoeff = data[:,1]
+            if i > 23 and i < 28:
+                rateCoeff /= 6.022e23**2
+            else:
+                rateCoeff /= 6.022e23
+
+            row_temp = []
+            data = []
+            header = ["", "Te(eV)", "", "kf(m3/s)", ""]
+            for j in range(len(Te)):
+                row_temp.append('(')
+                row_temp.append(Te[j])
+                row_temp.append(',')
+                row_temp.append(rateCoeff[j])
+                row_temp.append(')')
+                data.append(row_temp)
+                row_temp = []
+
+            g = open("./CSVs/RateProfile_{}.csv".format(i + 1), 'w')
+            writer = csv.writer(g)
+            writer.writerow(header)
+            writer.writerows(data)
+            g.close()
+        else:
+            continue
+
+row_temp = []
+data = []
+
+h = h5.File('../BOLSIGChemistry_Transport/nominal_transport.h5', 'r')
+Nmu = h["mobility"]
+Te_trans = Nmu[:,0]
+Te_trans /= 11604
+mobility = Nmu[:,1]
+
+header = ["", "Te(eV)", "", "Nmu(VMS)", ""]
+for k in range(len(Te_trans)):
+    row_temp.append('(')
+    row_temp.append(Te_trans[k])
+    row_temp.append(',')
+    row_temp.append(mobility[k])
+    row_temp.append(')')
+    data.append(row_temp)
+    row_temp = []
+
+z = open("./CSVs/Transport.csv", 'w')
+writer = csv.writer(z)
+writer.writerow(header)
+writer.writerows(data)
+z.close()
+
diff --git a/BOLSIGChemistry_Transport/nominal_transport.h5 b/BOLSIGChemistry_Transport/nominal_transport.h5
new file mode 100644
index 000000000..1e1557ecf
Binary files /dev/null and b/BOLSIGChemistry_Transport/nominal_transport.h5 differ
diff --git a/chebSolver.py b/chebSolver.py
index 6e654858f..2e0e8b0b6 100644
--- a/chebSolver.py
+++ b/chebSolver.py
@@ -3,17 +3,8 @@
 import time
 
 from Liu2014Properties import setLiu2014Properties
-from psaapProperties import setPsaapProperties
 from psaapPropertiesTestArm import setPsaapPropertiesTestArm
 from psaapPropertiesTestArmInterpTrans import setPsaapPropertiesTestArmInterpTrans
-from psaapPropertiesCurrentTestCase import setPsaapPropertiesCurrentTestCase
-from psaapPropertiesCurrentTestCase100mTorr import setPsaapPropertiesCurrentTestCase100mTorr
-from psaapPropertiesWithSampling import setPsaapPropertiesWithSampling
-from psaapPropertiesTestJP import setPsaapPropertiesTestJP
-from psaapPropertiesTestJP_Nominal import setPsaapPropertiesTestJP_Nominal
-from psaapPropertiesTestJP_Arrhenius import setPsaapPropertiesTestJP_Arrhenius
-from psaapProperties_6Species import setPsaapProperties_6Species
-from psaapProperties_6Species_Sampling import setPsaapProperties_6Species_Sampling
 from psaapProperties_6Species_Nominal import setPsaapProperties_6Species_Nominal
 
 class modelClosures:
@@ -197,16 +188,6 @@ def diffusivity(self, i, energy, mu, nb, EinsteinForm):
             V0 =  self.qStar * 1.0 # V0 = qStar * 1eV
             DEf = 2.0 / 3.0 * np.multiply(energy[:,[i]], mu[:,[i]]) / V0
 
-        # Einstein for electrons only (for testing purposes)
-        # if EinsteinForm:
-        #     #V0 =  self.qStar * 1.0 # V0 = qStar * 1eV
-        #     #DEf = 2.0 / 3.0 * np.multiply(energy[:,[i]], mu[:,[i]]) / V0
-        #     if self.Z[i] == -1:   ## Added this to use Einstein Relation only with electrons and constant values for heavies
-        #         V0 = self.qStar * 1.0
-        #         DEf = 2.0 / 3.0 * np.multiply(energy[:,[i]], mu[:,[i]]) / V0
-        #     else:
-        #         DEf[:,0] = self.D[i] / nb
-
         else:
             DEf[:,0] = self.D[i] / nb
 
@@ -228,19 +209,13 @@ def diffusivity_U(self, i, j, energy, energy_U, mu, D, nb, EinsteinForm):
             D_U_tmp = D_ee * np.diag(energy_U[i,j,:,:])
             D_U[:,:] = np.diag(D_U_tmp)
 
-        elif EinsteinForm:
+        elif EinsteinForm and self.Z[i] == -1:
             V0 =  self.qStar * 1.0 # V0 = qStar * 1eV
             D_U = 2.0 / 3.0 * np.multiply(mu[:,[i]], energy_U[i,j,:,:]) / V0
 
         if (j == self.Ns - 1):
             D_U[:,:] -= np.diag(D[:,i] / nb)
 
-        # Einstein for electrons only (for testing purposes)
-        # if EinsteinForm:
-        #     if self.Z[i] == -1:
-        #         V0 =  self.qStar * 1.0 # V0 = qStar * 1eV
-        #         D_U = 2.0 / 3.0 * np.multiply(mu[:,[i]], energy_U[i,j,:,:]) / V0
-
         return D_U
 
 
@@ -427,21 +402,27 @@ def __init__(self, Ns, NT, Np, elasticCollisionActivationFactor,
         elif(scenario==3):
             Nr = 8
         elif(scenario==4):
-            Nr = 8
+            Nr = 9
         elif(scenario==5):
             Nr = 7
         elif(scenario==6):
-            Nr = 9
+            Nr = 23
         elif(scenario==7):
-            Nr = 9
+            Nr = 23
         elif(scenario==8):
-            Nr = 9
+            Nr = 23
         elif(scenario==9):
             Nr = 23
         elif(scenario==10):
-            Nr = 23
+            Nr = 34
         elif(scenario==12):
             Nr = 23
+        elif(scenario==13):
+            Nr = 23
+        elif(scenario==14):
+            Nr = 23
+        elif(scenario==16):
+            Nr = 23
         elif(scenario==21):
             Nr = 8
         else:
@@ -463,21 +444,29 @@ def __init__(self, Ns, NT, Np, elasticCollisionActivationFactor,
         elif(scenario==3):
             setPsaapPropertiesCurrentTestCase(gam, V0, VDC, self.params, Nr, iSample)
         elif(scenario==4):
-            setPsaapPropertiesCurrentTestCase100mTorr(gam, V0, VDC, self.params, Nr, iSample)
+            setPsaapProperties_4Species_Nominal(gam, V0, VDC, self.params, Nr, iSample)
         elif(scenario==5):
             setPsaapPropertiesWithSampling(gam, V0, VDC, self.params, Nr, iSample)
         elif(scenario==6):
-            setPsaapPropertiesTestJP(gam, V0, VDC, self.params, Nr, iSample)
+            setPsaapProperties_6Species_Sampling(gam, V0, VDC, self.params, Nr, iSample)
         elif(scenario==7):
-            setPsaapPropertiesTestJP_Nominal(gam, V0, VDC, self.params, Nr, iSample)
+            setPsaapProperties_6Species_Sampling_250mTorr(gam, V0, VDC, self.params, Nr, iSample)
         elif(scenario==8):
-            setPsaapPropertiesTestJP_Arrhenius(gam, V0, VDC, self.params, Nr, iSample)
+            setPsaapProperties_6Species_Sampling_500mTorr(gam, V0, VDC, self.params, Nr, iSample)
         elif(scenario==9):
             setPsaapProperties_6Species(gam, V0, VDC, self.params, Nr, iSample)
         elif(scenario==10):
-            setPsaapProperties_6Species_Sampling(gam, V0, VDC, self.params, Nr, iSample)
+            setPsaapProperties_6Species_100mTorr_Expanded(gam, V0, VDC, self.params, Nr, iSample)
         elif(scenario==12):
             setPsaapProperties_6Species_Nominal(gam, V0, VDC, self.params, Nr, iSample)
+        elif(scenario==13):
+            setPsaapProperties_6Species_500mTorr(gam, V0, VDC, self.params, Nr, iSample)
+        elif(scenario==14):
+            setPsaapProperties_6Species_1Torr_Expanded(gam, V0, VDC, self.params, Nr, iSample)
+        elif(scenario==15):
+            setPsaapProperties_6Species_Sampling_1Torr_Expanded(gam, V0, VDC, self.params, Nr, iSample)
+        elif(scenario==16):
+            setPsaapProperties_6Species_5Torr(gam, V0, VDC, self.params, Nr, iSample)
         elif(scenario==21):
             setPsaapPropertiesTestArmInterpTrans(gam, V0, VDC, self.params, Nr, iSample)
 
@@ -1865,6 +1854,7 @@ def plot(self, col, create=True):
                         action='store_true', help="Activate Einstein's form for diffusion coefficient.")
     parser.add_argument('--iSample', metavar='iSample', default=0,
                         type=int, help='Sample index, if BOLSIG chemistry is used.')
+    parser.add_argument('--gam', metavar='gam', default=0.01, type=float, help='Secondary Electron Emission Coefficient')
     args = parser.parse_args()
 
     # Dump inputs to the screen for posterity
@@ -1907,26 +1897,34 @@ def plot(self, col, create=True):
         print("#   Running scenario = 3 (4 species, 8 rxn, Liu 2017)")
         Ns = 4
     elif(args.scenario==4):
-        print("#   Running scenario = 4 (4 species, 8 rxn, Liu 2017)")
+        print('#   Running scenario = 4 (4 species, 9 rxn, 1Torr, Nominal)')
         Ns = 4
     elif(args.scenario==5):
-        print("#   Running scenario = 5 (4 species, 7 rxn, Bolsing and Lay, Moss et al, 2003)")
+        print('#   Running scenario = 5 (4 species, 9 rxn, 1Torr, Sampling)')
         Ns = 4
     elif(args.scenario==6):
-        print("#   Running scenario = 6 (4 species, 9 rxn, Juan's mechanism)")
-        Ns = 4
+        print('#   Running scenario = 6 (6 species, 23 rxn, 1Torr, 100V, Sampling)')
+        Ns = 6
     elif(args.scenario==7):
-        print("#   Running scenario = 7 (4 species, 9 rxn, Nominal reaction rates)")
-        Ns = 4
+        print('#   Running scenario = 7 (6 species, 23 rxn, 250mTorr, 100V, Sampling)')
+        Ns = 6
     elif(args.scenario==8):
-        Ns = 4
+        print('#   Running scenario = 8 (6 species, 23 rxn, 500mTorr, 100V, Sampling)')
+        Ns = 6
     elif(args.scenario==9):
         print("#   Running scenario = 9 (6 species, 23 rxn)")
         Ns = 6
     elif(args.scenario==10):
+        print('#   Running scenario = 10 (6species, 34 rxn, 100mTorr, Nominal)')
         Ns = 6
     elif(args.scenario==12):
+        print('#   Running scenario = 12 (6 species, 23 rxn, 1Torr, 100V, Nominal)')
+        Ns = 6
+    elif(args.scenario==13):
+        print('#   Running scenario = 13 (6 species, 23 rxn, 500mTorr, 100V, Nominal)')
         Ns = 6
+    elif(args.scenario==14):
+        print('#   Running scenario = 14 (6 species, 34 rxn, 1Torr, Nominal)')
     elif(args.scenario==21):
         print("#   Running scenario = 21 (4 species, 8 rxn, Liu 2017, interpolated transport)")
         Ns = 4
@@ -1970,7 +1968,7 @@ def plot(self, col, create=True):
     # Instantiate solver class
     tds = timeDomainCollocationSolver(Ns, 1, args.Np, elasticCollisionActivationFactor,
                                       backgroundSpecieActivationFactor, EinsteinForm,
-                                      gam=0.01, V0 = args.V0, VDC = args.VDC,
+                                      gam=args.gam, V0 = args.V0, VDC = args.VDC,
                                       scenario=args.scenario, scheme=args.tscheme,
                                       iSample = args.iSample)
 
diff --git a/fullRun.sh b/fullRun.sh
deleted file mode 100755
index e0dc2d977..000000000
--- a/fullRun.sh
+++ /dev/null
@@ -1,39 +0,0 @@
-#!/bin/bash
-
-error_exit()
-{
-  echo "$1" 1>&2
-  exit 1
-}
-
-EXE="python3 ./chebSolver.py"
-#Np=150
-#Nt=32000
-#dt=0.015625
-
-# Liu 3 species case
-Np=150
-Nt=64000
-dt=0.0078125
-baseFile='restart_3spec_Np150_'
-
-baseCmd="$EXE --Np $Np --Nt $Nt --dt $dt"
-screenOut="fullRun.out"
-rm -f $screenOut
-
-# Run Chebyshev time domain solver out to 4000 periods
-$baseCmd --t0    0.0 --outfile "${baseFile}T0500.npy" > $screenOut || error_exit "First run failed"
-$baseCmd --t0  500.0 --restart "${baseFile}T0500.npy" \
-	             --outfile "${baseFile}T1000.npy" >> $screenOut || error_exit "Second run failed"
-$baseCmd --t0 1000.0 --restart "${baseFile}T1000.npy" \
-	             --outfile "${baseFile}T1500.npy" >> $screenOut || error_exit "Third run failed"
-$baseCmd --t0 1500.0 --restart "${baseFile}T1500.npy" \
-	             --outfile "${baseFile}T2000.npy" >> $screenOut || error_exit "Fourth run failed"
-$baseCmd --t0 2000.0 --restart "${baseFile}T2000.npy" \
-	             --outfile "${baseFile}T2500.npy" >> $screenOut || error_exit "Fifth run failed"
-$baseCmd --t0 2500.0 --restart "${baseFile}T2500.npy" \
-	             --outfile "${baseFile}T3000.npy" >> $screenOut || error_exit "Sixth run failed"
-$baseCmd --t0 3000.0 --restart "${baseFile}T3000.npy" \
-	             --outfile "${baseFile}T3500.npy" >> $screenOut || error_exit "Seventh run failed"
-$baseCmd --t0 3500.0 --restart "${baseFile}T3500.npy" \
-	             --outfile "${baseFile}T4000.npy" >> $screenOut || error_exit "Eighth run failed"
diff --git a/generate_sample.py b/generate_sample.py
index 4c33dc885..e0ce02f3c 100644
--- a/generate_sample.py
+++ b/generate_sample.py
@@ -47,7 +47,7 @@ def getNumberOfReactants(equation):
                     # Take the nominal value from the input file
                     coeffs0 = np.array(rxn['arrhenius']['coefficients'], dtype = np.double)
                     # Add a relative error
-                    coeffs0[0] *= (1.0 + error) ** np.random.normal()
+                    coeffs0[0] *= (1.0 + error) ** np.random.normal(0, 1./3)
                     # Create a dataset for the reaction
                     dset = f.create_dataset(rxn['equation'], (3,), data = coeffs0)
                     dset.attrs['rate_unit'] = rateUnit
diff --git a/plotSampleRun.ipynb b/plotSampleRun.ipynb
deleted file mode 100644
index be328070d..000000000
--- a/plotSampleRun.ipynb
+++ /dev/null
@@ -1,207 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plot sample results\n",
-    "This notebook shows plots from the run conducted in fullRun.sh or sampleRun.sh, depending on what is available.  You need to run one of those scripts prior to using this notebook in order to generate the data files used here.\n",
-    "\n",
-    "Two sets of plots are generated.  The first shows the time histories of $\\min(n_e)$, $\\max(n_e)$, $\\min(T_e)$ and $\\max(T_e)$.  This data is dumped to the screen for every time step, which is captured in run.out (for sampleRun.sh) or fullRun.out (for fullRun.sh).  The second set of plots shows $n_e$, $n_i$, and $T_e$ as a function of $x$ for the snapshots stored in the restart files."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x648 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from os import path\n",
-    "\n",
-    "## check what result is available\n",
-    "#if (path.isfile('fullRun.out')):\n",
-    "#    # if we have a long run, plot that\n",
-    "#    D = np.loadtxt('fullRun.out')\n",
-    "#elif (path.isfile('run.out')):\n",
-    "#    # otherwise, try short run\n",
-    "#    D = np.loadtxt('run.out')\n",
-    "#else:\n",
-    "#    print(\"Don't have any data!  Plotting will fail.\")\n",
-    "\n",
-    "    \n",
-    "D = np.loadtxt('run.out')\n",
-    "\n",
-    "plt.figure(figsize=(16,9))\n",
-    "ax0 = plt.subplot(4,1,1)\n",
-    "ax0.plot(D[:,0], D[:,1], 'b-')\n",
-    "plt.setp(ax0.get_xticklabels(),visible=False)\n",
-    "plt.setp(ax0.get_yticklabels(),fontsize=12)\n",
-    "ax0.set_ylabel(r'$\\min(n_e)$', fontsize=16)\n",
-    "\n",
-    "ax1 = plt.subplot(4,1,2,sharex=ax0)\n",
-    "ax1.plot(D[:,0], D[:,2], 'b-')\n",
-    "#ax1.set_ylim(0.85,1.1)\n",
-    "ax1.grid()\n",
-    "plt.setp(ax1.get_xticklabels(),visible=False)\n",
-    "plt.setp(ax1.get_yticklabels(),fontsize=12)\n",
-    "ax1.set_ylabel(r'$\\max(n_e)$', fontsize=16)\n",
-    "\n",
-    "ax2 = plt.subplot(4,1,3,sharex=ax0)\n",
-    "ax2.plot(D[:,0], D[:,3], 'b-')\n",
-    "plt.ylim(0,1e-5)\n",
-    "plt.setp(ax2.get_xticklabels(),visible=False)\n",
-    "plt.setp(ax2.get_yticklabels(),fontsize=12)\n",
-    "ax2.set_ylabel(r'$\\min(T_e)$', fontsize=16)\n",
-    "\n",
-    "ax3 = plt.subplot(4,1,4,sharex=ax0)\n",
-    "ax3.plot(D[:,0], D[:,4], 'b-')\n",
-    "#plt.ylim(0,1e-5)\n",
-    "plt.setp(ax3.get_xticklabels(),fontsize=12)\n",
-    "plt.setp(ax3.get_yticklabels(),fontsize=12)\n",
-    "ax3.set_ylabel(r'$\\max(T_e)$', fontsize=16)\n",
-    "ax3.set_xlabel(r'$t$', fontsize=16)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# The non-dimensional transport and chemstry properties are\n",
-      "#   De    = 3.536159e-01\n",
-      "#   Di    = 1.896334e-05\n",
-      "#   mue   = 8.849558e+01\n",
-      "#   mui   = 4.259880e-01\n",
-      "#   Ck    = 2.932670e+02\n",
-      "#   A     = 2.803050e+01\n",
-      "#   dH    = 1.570000e+01\n",
-      "#   qStar = 1.000000e+03\n",
-      "#   alpha = 3.611738e+01\n",
-      "#   ks    = 1.755162e+00\n",
-      "#   gam   = 1.000000e-02\n",
-      "['restart_psaap_Np250_T10.npy', 'restart_psaap_Np250_T20.npy', 'restart_psaap_Np250_T30.npy', 'restart_psaap_Np250_T40.npy', 'restart_psaap_Np250_T50.npy']\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x1944 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import os, fnmatch\n",
-    "import chebSolver as cs\n",
-    "\n",
-    "tds = cs.timeDomainCollocationSolver(2,1,250)\n",
-    "\n",
-    "#files = fnmatch.filter(os.listdir('.'), 'restart_Np150_*.npy')\n",
-    "files = fnmatch.filter(os.listdir('.'), 'restart_psaap_Np250_*.npy')\n",
-    "files.sort()\n",
-    "print(files)\n",
-    "cols = ['b-', 'r-', 'g-', 'm-', 'c-', 'k-', 'b--', 'r--', 'g--']\n",
-    "\n",
-    "files.append('newton_psaap_Np250.npy')\n",
-    "\n",
-    "#or file, col in zip(files, cols):\n",
-    "#    print(\"Plotting {0:s} with {1:s}\".format(file,col),flush=True)\n",
-    "\n",
-    "create = True\n",
-    "for file, col in zip(files, cols):\n",
-    "    tds.U2 = np.load(file)\n",
-    "    tds.plot(col,create)\n",
-    "    create = False\n",
-    "    \n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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3PenjH3ehzydjONkWAADAp8OH3ZrGjuf8+VJjI/s8k6yo4GmMmSJpjaTvnvfh+yStMcZ0FH7vvvDLQ5Qee8yNJPg8VOh8BE8AAAB/0tzxNMZ1Pel4JldRwdNa22etnWmt7TnvY0ettauttYsL67HoykQUHn/c/WC58UbflTgETwAAAH+C4Dl7xCNDk+/mm6WdOyXOM02mUk+1RUacOyf95CfSu9/trlJJgrY26ehR6cQJ35UAAADkT0+Puw9zyhTflZTnLW9x61NPeS0Do0hI5EDcfvlL6c03pTvu8F3JsOBk2717vZYBAACQS729Ul2d7yrKd/PN0syZ0ve/77sSjITgmVM/+pFUVSWtWeO7kmFB8Ny922sZAAAAuZT24Fld7W5q+OEP3Z2kSBaCZ049/rh0yy3uKpWkaGlxKx1PAACA+KU9eErSv/7XbmT45z/3XQkuRvDMocOHpV/9KlljtpI0Y4Zb33zTbx0AAAB5lIXguWaNVFPjrgxEshA8c+jHP3bre97jt46LjR8v1dYSPAEAAHzIQvCcNMk1V77/fWlw0Hc1OB/BM4d+9CN3we6KFb4rudT06dLx476rAAAAyJ8sBE/Jjdt2dXGnZ9IQPHPm7FnpiSdct9MY39Vcqr6ejicAAIAPWQme732vuxaGcdtkIXjmzDPPuA3X73+/70pGRscTAADAj6wEz7o66e1vd1N+SA6CZ848+qibfU/SNSrno+MJAAAQv4EB95aF4Cm52xtee006edJ3JQgQPHPEWhc8g9O+koiOJwAAQPxOnHBrVoLnDTe4576bN/uuBAGCZ45s3ix1dkp33um7ktHR8QQAAIhfb69bsxI8r7/erZs2+a0DwwieOfLYY+5Aofe9z3clo5s+3f3g4/hrAACA+GQteDY2SnPmuLvrkQwEzxx59FE37z5nju9KRldf79aeHr91AAAA5EnWgqcxrutJ8EwOgmdO7Nvn/uElecxWGg6e7PMEAACITxA8p03zW0eYrr9eevVVqb/fdyWQCJ658dhjbk168Jw+3a3s8wQAAIhP1jqekjtgaHBQevll35VAInjmxpNPSgsXSlde6buSy6PjCQAAEL8sBs/ggCHGbZOB4JkDQ0PSM89It9/uu5Kx0fEEAACIXxaD5/z50owZBM+kIHjmwCuvuCB3222+KxkbHU8AAID49fZKVVXS5Mm+KwkPBwwlC8EzB556yq1pCJ50PAEAAOLX2+u6ncb4riRc11/vmjADA74rAcEzB55+Wmprc+MGSVdTI1VX0/EEAACIUxA8s+aGG6SzZ6WtW31XAoJnxg0NueCZhv2dknuVbfp0Op4AAABx6unJZvC88Ua3Pvec3zpA8My8rVulY8fSMWYbqK+n4wkAABCnrHY829rc27p1visBwTPjnn7arWkKnnQ8AQAA4pXV4ClJ73yn9POfS+fO+a4k3wieGffUU1JLi9Ta6ruS4tHxBAAAiFeWg+eaNe5/3y9/6buSfCN4Zpi1ruOZpm6nRMcTAAAgblkOnu94hztH5Kc/9V1JvhE8M2zrVumNN9IXPOl4AgAAxCvLwXPmTHetypNP+q4k3wieGRZsol692m8dpQo6ntb6rgQAACD7zp2T+vqyGzwlt8/z+eelEyd8V5JfBM8MW7dOWrjQ7fFMk/p66cwZ6fRp35UAAABkXxDGshw816xxATs4eBPxI3hmVPAPK23dTsl1PCX2eQIAAMSht9etWQ6et94qTZrEPk+fCJ4Z1d7ufoikMXjW17uVfZ4AAADRy0PwnDTJhc9nnvFdSX4RPDMq2N/5jnf4raMcdDwBAADik4fgKUnXXecO3+Q+Tz8Inhm1bp20cqU0a5bvSkpHxxMAACA+eQme117rzhHZvt13JflE8Mygvj7puefSOWYr0fEEAACIU56CpyS98orfOvKK4JlBzz3nXs1Ja/Ck4wkAABCfvATPpUulqirp5Zd9V5JPBM8M+tnPpOpq6dd/3Xcl5Zk2za10PAEAAKKXl+A5caILnwRPPwieGfSLX0irVkm1tb4rKc/48a52Op4AAADRC4JnWp87luLaaxm19YXgmTFnzrirVN7yFt+VVGb6dDqeAAAAcejtlaZOlcblIBlce620d6/U0+O7kvzJwV+vfHnxRWlgIP3Bs76ejicAAEAcenuzP2YbuOYat9L1jB/BM2M2bHDrLbf4raNSdDwBAADi0ds7fMZG1nGyrT8Ez4zZsEGaP1+64grflVSGjicAAEA88tTxbGpyDQ4OGIofwTNj1q9P/5itRMcTAAAgLnkKnsa4rifBM34EzwzZv9+9pX3MVqLjCQAAEJeenvwET8nt83zlFcla35XkC8EzQ4L9nVnpePb0SIODvisBAADItjx1PCXX8TxxQtq923cl+ULwzJD166XJk6UVK3xXUrn6ercG90oBAAAgGnkLnjfd5Nbnn/dbR94QPDNkwwZp1Spp/HjflVRu+nS3ss8TAAAgOkNDrvuXp+B5zTXu3tJnn/VdSb4UFTyNMdONMd8xxrxmjNlmjLnFGDPDGPOkMaajsNZHXSxGd/q09KtfZWPMVhrueLLPEwAAIDonT7o1T8GzqsqdifLcc74ryZdiO55fkfRja+1SSSskbZN0r6R11trFktYV3ocnmzZJZ89m42AhiY4nAABAHIJtTXkKnpJ0663ugCGaHPEZM3gaY+okvU3S1yTJWnvGWntc0p2SHip82kOS7oqqSIwtOFgoK8GTjicAAED08ho83/pWd6ot+zzjU0zHc4GkbkkPGmNeNMb8vTGmRtIca+0hSSqssyOsE2NYv15auFCanZE/BTqeAAAA0ctr8Py1X3Mjt+zzjE8xwbNa0vWS/tZae52kUyphrNYYc7cxpt0Y097d3V1mmbgca13HMyvdTomOJwAAQBzyGjxraqTrrmOfZ5yKCZ77Je231r5QeP87ckG0yxjTKEmF9chIX2ytvd9au8pau6qhoSGMmnGRvXulw4ezc7CQ5H4YVFXR8QQAAIhSXoOn5PZ5vvCCOycF0RszeFprD0vaZ4y5svCh1ZJelfSYpLWFj62V9GgkFWJM69e7NUsdT2Nc15OOJwAAQHTyHDzf+lapv1968UXfleRDdZGf92lJ3zTGTJC0S9In5ULrI8aYT0nqlPThaErEWDZskGprpauv9l1JuKZPp+MJAAAQpTwHz1tvdeuzz0o33eS3ljwoKnhaa1+StGqE31odbjkox/r17h9LdbEvI6QEHU8AAIBoBcFz6lS/dfjQ2CjNneuuVUH0ir3HEwl16pS0eXO2xmwD06bR8QQAAIhSb680ZUr2GhjFWr5cevVV31XkA8Ez5drbpcHBbB0sFJg61QVrAAAARKO3N59jtoGrrnLB01rflWQfwTPlNmxw66/9mt86olBTI5086bsKAACA7CJ4uueb+/f7riT7CJ4p194uLVwozZzpu5Lw1dbS8QQAAIgSwdOtjNtGj+CZcps2SatGOvYpA+h4AgAARIvg6datW/3WkQcEzxQ7elTas0e64QbflUSjpkbq65OGhnxXAgAAkE15D56zZkmzZ9PxjAPBM8U2bXJrVjuetbVu7e/3WwcAAEBW5T14SsMHDCFaBM8Ua2936/XX+60jKjU1bmXcFgAAIBq9vfm8w/N8nGwbD4JnirW3S4sXu/susyjoeHLAEAAAQDT6+oZf7M+rq66SenqkQ4d8V5JtBM8Uy/LBQhIdTwAAgCgNDkoDA9KUKb4r8Wv5crcybhstgmdKdXdLnZ3ZPVhIGg6edDwBAADCF5yjkffgycm28SB4plTWDxaSGLUFAACIEsHTaWiQZs6k4xk1gmdKBQcLXXed3zqixKgtAABAdPr63Dp5st86fDPGdT23bPFdSbYRPFOqvV268spsH3/NqC0AAEB0guCZ946n5G6JeOkl6dw535VkF8EzpTZtyvb+Tml41JaOJwAAQPgInsNuvNH999i2zXcl2UXwTKGuLmn//mzv75ToeAIAAESJ4DnsxhvdunGj3zqyjOCZQnk4WEgieAIAAESJ4Dls0SK3hY3gGR2CZwq1t7tN0Fk+WEiSqquliRMZtQUAAIgCwXPYuHGuqRMc4InwETxTqL1dWrp0eA9kltXU0PEEAACIAtepXOjGG6XNm6WBAd+VZBPBM4XycLBQoLaWjicAAEAU6HheaNUq6exZ6eWXfVeSTQTPlDl0SDp4MPv7OwN0PAEAAKLBPZ4X4oChaBE8UyYvBwsFamsJngAAAFGg43mh+fOlhgaCZ1QIninT3u42P69c6buSeNTUMGoLAAAQhSB4Tprkt46kMMZ1PTlgKBoEz5Rpb5eWLRu+aiTrGLUFAACIRl+f63Ya47uS5LjhBunVV6XTp31Xkj0EzxSxNl8HC0kcLgQAABCVIHhi2LJl0tCQtGOH70qyh+CZIocOSYcP5yt40vEEAACIBsHzUlde6dbXX/dbRxYRPFNk82a35mV/p8ThQgAAAFHp7yd4XmzJEre+9prfOrKI4JkiQfC89lq/dcSJw4UAAACi0dfHVSoXq62VmproeEaB4JkimzdLLS3S9Om+K4lPTY107px05ozvSgAAALKFUduRLV1KxzMKBM8U2bxZWrHCdxXxqq11K11PAACAcBE8R3blla7jaa3vSrKF4JkS/f3uH0DegmdwbQz7PAEAAMJF8BzZ0qVSb6871BPhIXimxJYt7mjnPB0sJA13PAmeAAAA4SJ4joyTbaNB8EyJ4GChvHY8GbUFAAAIF8FzZEuXupV9nuEieKbE5s2u+9fW5ruSeDFqCwAAEA2uUxnZvHnuvwsdz3ARPFNi82Z3jcq4nP2JcbgQAABANOh4jmzcODduS8czXDmLMelkrfTyy/kbs5XoeAIAAERhcFAaGOAez9EEJ9siPATPFNi7V+rpyWfw5HAhAACA8PX3u5WO58iWLpX27JFOn/ZdSXYQPFMgrwcLSRwuBAAAEIW+PrcSPEe2dKmbOmTcNjwEzxTYvFkyRrrmGt+VxI9RWwAAgPARPC8vaPgEDSBUjuCZAi+9JC1aNBzC8mTSJLfBm44nAABAeAiel7d4sdv/SvAMD8EzBTZvzueYreQ6vTU1dDwBAADCRPC8vKoqN2340ku+K8kOgmfC9fZKu3ZJK1f6rsSf2lqCJwAAQJg4XGhsK1e6BpC1vivJBoJnwr3yilvz2vGUXMeTUVsAAIDw0PEc24oV0rFj0v79vivJBoJnwuX5RNsAo7YAAADhCoIn93iOLpg4ZNw2HATPhNu8Waqvl5qafFfiT20tHU8AAIAw0fEcW3CjBAcMhYPgmXDBwULG+K7EHzqeAAAA4SJ4jm3qVHezBB3PcBA8E2xw0O3xzPOYrcQeTwAAgLARPIuzYgUdz7AUFTyNMXuMMa8YY14yxrQXPjbDGPOkMaajsNZHW2r+7NzpfijkPXhyqi0AAEC4CJ7FWblS2rFDOnHCdyXpV0rH8+3W2pXW2lWF9++VtM5au1jSusL7CFHQ1s978GTUFgAAIFx9fW4r18SJvitJtuB5eHDTBMpXyajtnZIeKvz6IUl3VV4OzvfKK+7y2quu8l2JXxwuBAAAEK7+ftftzPM5IsVYtsytr7/ut44sKDZ4WklPGGM2GWPuLnxsjrX2kCQV1tkjfaEx5m5jTLsxpr27u7vyinNk61Zp8WJp0iTflfhVU+N+OA4O+q4EAAAgG/r6uEqlGK2tUnW11NHhu5L0qy7y82611h40xsyW9KQx5rViH8Bae7+k+yVp1apVtowac2vLFunaa31X4V9NjVv7+tzpYgAAAKhMXx/7O4tRXS21tRE8w1BUx9Nae7CwHpH0PUk3SeoyxjRKUmE9ElWRedTf7w4Xuvpq35X4V1vrVvZ5AgAAhIPgWbzFiwmeYRgzeBpjaowxU4NfS3qXpC2SHpO0tvBpayU9GlWRefTaa9LQkLR8ue9K/As6ngRPAACAcBA8i7dkiQueltnNihQzajtH0veM23lcLelb1tofG2M2SnrEGPMpSZ2SPhxdmfmzZYtb6XgOdzw5YAgAACAcBM/iLV7s/nsdPCjNm+e7mvQaM3haa3dJuuRCD2vtUUmroygK7mCh8eOlRYt8V+IfHU8AAIBw9fVJ06b5rrg91B4AACAASURBVCIdFi92a0cHwbMSlVyngght2SItXerCZ94FwZOOJwAAQDiC61QwtvODJ8pH8EyorVvZ3xngcCEAAIBwMWpbvOZmaeJEgmelCJ4JdPKktGcP+zsDjNoCAACEi3s8i1dVJS1cKG3f7ruSdCN4JtCrr7qV4OlwuBAAAEC46HiWhitVKkfwTKDgRFtGbR06ngAAAOEieJZmyRJp50533SHKQ/BMoK1b3ehDW5vvSpKBw4UAAADCc+6cdOYMwbMUixdLAwPSvn2+K0kvgmcCbdkiLVvm5snh/jtMmkTHEwAAIAz9/W4leBYvONmWfZ7lI3gm0Nat7O+8WE0NwRMAACAMfX1uJXgW76qr3BqcxYLSETwT5vhx6cAB9nderLaWUVsAAIAw0PEs3ezZ0qxZw2exoHQEz4TZutWtdDwvRMcTAAAgHHQ8y3P11cPP1VE6gmfCcKLtyGpq6HgCAACEIQie3ONZmuXLXfC01ncl6UTwTJitW91Y6fz5vitJltpaOp4AAABhoONZnquvlnp7pf37fVeSTgTPhNmyxf2lNsZ3JcnCqC0AAEA4CJ7lCSYS2edZHoJnwmzdypjtSDhcCAAAIBwEz/IEz9HZ51kegmeCdHdLR45wsNBI6HgCAACEg+BZnhkzpMZGOp7lIngmSPDqCR3PS3G4EAAAQDi4TqV8nGxbPoJnggSvntDxvFRwuBCniAEAAFSGjmf5li+XXn1VGhryXUn6EDwTZOtWqb5emjvXdyXJU1MjDQ5KZ874rgQAACDduE6lfFdf7f777dnju5L0IXgmyLZt0rJlnGg7ktpatzJuCwAAUJm+Pvd8c+JE35Wkz1VXufXVV/3WkUYEzwQJgicuVVPjVg4YAgAAqExfnxuzpdlRukWL3Lpzp9860ojgmRDHjrkTbQmeIwuCJx1PAACAygTBE6WbNUuqqyN4loPgmRCvvebWpUv91pFUdDwBAADCQfAsnzHSwoXSjh2+K0kfgmdCbNvmVjqeIwuCZ7AZHgAAAOUheFZm4UI6nuUgeCbEa69JkyZJLS2+K0kmOp4AAADh6O8neFZi0SJp92534wKKR/BMiG3bpCVLpKoq35UkU/DDkeAJAABQGTqelVm4UDp7Vtq3z3cl6ULwTAhOtL08Op4AAADh6OvjDs9KBCfbss+zNATPBDh92rXrCZ6jY48nAABAOOh4VmbhQreyz7M0BM8E2L5dspYTbS+HjicAAEA4CJ6VmTdPmjiR4FkqgmcCcKLt2IJxEIInAABAZQielRk3TlqwgFHbUhE8E+C119ydQEuW+K4kuaqq3Km/jNoCAABUhuBZOa5UKR3BMwG2bZPa2lywwuhqauh4AgAAVIrgWblFi1zwtNZ3JelB8EwATrQtzpQpBE8AAIBKnDvnrgIheFZm4UL3vLSry3cl6UHw9Gxw0B0uRPAcGx1PAACAyvT3u5XgWZngSpXt2/3WkSYET8/27nXXqXCi7dhqatjjCQAAUInguRT3eFYmeO7++ut+60gTgqdnnGhbPDqeAAAAlQmCJx3Pysyf78L7a6/5riQ9CJ6eBX9Z6XiOjT2eAAAAlSF4hmPcOOnKK4ebSBgbwdOzbduk2bOlGTN8V5J8jNoCAABUhuAZnqVL6XiWguDpGSfaFo9RWwAAgMoQPMOzdKm0Z8/wgU24PIKnR9YSPEvBqC0AAEBlONU2PEuXuufzHR2+K0kHgqdH3d3Sm2+yv7NYdDwBAAAqQ8czPMFzeMZti0Pw9IgTbUtTU+NepRsa8l0JAABAOnGdSniWLJGMIXgWi+DpEcGzNDU1bmWOHgAAoDx0PMMzebLU2srJtsUieHr0+uvuH/28eb4rSYfgByTjtgAAAOUheIaLk22LR/D0aPt216Ifx59CUYKOJ8ETAACgPEHwDJ5XoTJLl7pmElvBxkbk8SgInihO8AOSuzwBAADKc+qUVFUljR/vu5JsWLLEbQM7cMB3JclH8PTkzBlp926CZykYtQUAAKhMX597TmWM70qyYeFCt+7e7beONCg6eBpjqowxLxpjflB4f4Yx5kljTEdhrY+uzOzZvVsaHCR4loJRWwAAgMoEwRPhaGtz665dfutIg1I6np+RdP6ZTfdKWmetXSxpXeF9FOn119165ZV+60gTRm0BAAAqc+oU+zvDNH++O6+F4Dm2ooKnMaZJ0nsl/f15H75T0kOFXz8k6a5wS8u27dvdunix3zrShI4nAABAZeh4hmvCBKm5meBZjGI7nl+W9FlJ55/XNMdae0iSCuvskGvLtO3bpYYGqZ4B5aKxxxMAAKAyBM/wLVhA8CzGmMHTGPM+SUestZvKeQBjzN3GmHZjTHt3d3c53yKTONG2dHQ8AQAAKkPwDB/BszjFdDxvlfQBY8weSf8o6R3GmG9I6jLGNEpSYT0y0hdba++31q6y1q5qaGgIqez0I3iWjj2eAAAAlWGPZ/gWLJC6uniOOpYxg6e19nPW2iZrbaukj0r6mbX230h6TNLawqetlfRoZFVmzIkT0qFDBM9STZ7sVjqeAAAA5aHjGb4FC9zKlSqXV8k9nvdJWmOM6ZC0pvA+ihAcLETwLM24cS58EjwBAADKQ/AMH1eqFKe6lE+21j4l6anCr49KWh1+SdnHibblq6lhjAEAAKBcfX2M2oYt6HgSPC+vko4nytTRIRkjLVrku5L0qamh4wkAAFCuU6foeIZt1iyptpbgORaCpwfbt7v7foI9iyjelCkETwAAgHIMDUn9/QTPsBnDybbFIHh60NHBmG256HgCAACU5/RptxI8w7dggbRzp+8qko3gGTNruUqlEuzxBAAAKE/wHIo9nuFbtMh1PAcHfVeSXATPmB09Kh0/TsezXHQ8AQAAyhM8h6LjGb4lS6SBAWnfPt+VJBfBM2YdHW6l41ke9ngCAACUJ+h4EjzDFzSVguf6uBTBM2ZcpVIZRm0BAADKQ/CMTtBUCp7r41IEz5h1dEhVVcMXzaI0jNoCAACUJ3gOxR7P8DU2uv+udDxHR/CMWUeHC53jx/uuJJ0YtQUAACgPHc/oGOMmGul4jo7gGbPt2xmzrURNjTsKnBPDAAAASkPwjNaSJXQ8L4fgGSNr3V9GDhYqXzAa0t/vtw4AAIC0IXhGa/Fiafdu6exZ35UkE8EzRocOuTFROp7lC4In47YAAAClYY9ntJYscVN5u3f7riSZCJ4xClrvBM/yBa/QETwBAABKQ8czWsFzfPZ5jozgGSPu8KwcHU8AAIDyEDyjFTzHZ5/nyAieMdq+XZowQWpu9l1JegXBk7s8AQAAStPX567143aFaMycKdXX0/EcDcEzRh0d0qJF7h88ysOoLQAAQHlOnXIv4hvju5LsWrBA2rPHdxXJRPCMEVepVI5RWwAAgPL09TFmG7W2Ng4XGg3BMyZDQ9LOnQTPSjFqCwAAUB6CZ/RaW13Hc2jIdyXJQ/CMyb590sAABwtVio4nAABAefr6uEolam1t7jl/V5fvSpKH4BmTYJMxHc/KsMcTAACgPKdO0fGMWmurWxm3vRTBMybc4RkOOp4AAADlYdQ2em1tbiV4XorgGZOODvcP/YorfFeSbpMnu5PY2OMJAABQGoJn9IKOJyfbXorgGZPgRFuOr66MMe4HJh1PAACA0rDHM3qTJ0tz5tDxHAnBMyYdHYzZhoXgCQAAUDr2eMaDK1VGRvCMwblz7i8fwTMcNTWM2gIAAJSKUdt4BFeq4EIEzxh0drrwuWiR70qyoaaGjicAAECpCJ7xaGtzz/8HB31XkiwEzxjs3OnWhQv91pEVjNoCAACUZmhI6u9nj2cc2tpc02n/ft+VJAvBMwY7driVjmc46HgCAACUpr/frXQ8o8ddniMjeMZgxw53wlVjo+9KsoE9ngAAAKUJnjsRPKO3YIFbd+3yW0fSEDxjsHOn+ws4jv/aoaDjCQAAUBqCZ3xaWqTq6uHtdnCIQjHYsYMx2zCxxxMAAKA0wXMn9nhGr7rahc9gux0cgmfEhoZcm52DhcJDxxMAAKA0dDzjtXAhHc+LETwjduiQ28xNxzM87PEEAAAoDcEzXosWuY6ntb4rSQ6CZ8SCFjsdz/BMmSINDHA3EgAAQLEInvFauFDq6ZGOHfNdSXIQPCMWtNjpeIYn2JvAuC0AAEBx2OMZr6DpxLjtMIJnxHbscBuM58/3XUl2BD8wGbcFAAAoDh3PeAVNJ4LnMIJnxHbudJfIVlf7riQ76HgCAACUhuAZr+AuT062HUbwjBhXqYQv+IFJ8AQAAChOEDwZtY3H5MnSFVfQ8TwfwTNC1rrgycFC4aLjCQAAUJrgeRMdz/gsWkTwPB/BM0JHj0q9vXQ8w8YeTwAAgNL09bmtX+PH+64kPxYuZNT2fATPCHGVSjQYtQUAAChNXx/dzri1tkqHD0unT/uuJBkInhHiKpVoMGoLAABQmlOn2N8Zt9ZWt+7b57WMxCB4RmjHDskYqa3NdyXZwqgtAABAaeh4xq+lxa179ngtIzEInhHasUNqapImTfJdSbbQ8QQAACgNwTN+Qcdz716vZSQGwTNCO3cyZhsF9ngCAACUhuAZv3nzpKoqOp4BgmeEuEolGpMmuRFmgicAAEBx2OMZv+pqFz7peDoEz4j09krd3XQ8o2CM+8HJHk8AAIDi0PH0o7WV4BkYM3gaYyYZY35pjNlsjNlqjPmTwsdnGGOeNMZ0FNb66MtNj+BEWzqe0aipoeMJAABQLIKnHy0tjNoGiul4Dkh6h7V2haSVkt5tjLlZ0r2S1llrF0taV3gfBbt2uZXgGY3aWunECd9VAAAApAPB04/WVunAAensWd+V+Ddm8LTOycK74wtvVtKdkh4qfPwhSXdFUmFKBcFzwQK/dWRVXR3BEwAAoFjs8fSjpUUaGpL27/ddiX9F7fE0xlQZY16SdETSk9baFyTNsdYekqTCOnuUr73bGNNujGnv7u4Oq+7E27VLmjFDmjbNdyXZVFcn9fT4rgIAACAd6Hj6wZUqw4oKntbaQWvtSklNkm4yxlxd7ANYa++31q6y1q5qaGgot87U2bWLbmeUpk1zBzgBAADg8oaGpNOnCZ4+tLS4lX2eJZ5qa609LukpSe+W1GWMaZSkwnok9OpSbPdugmeU6uoIngAAAMXo73crwTN+zc3uRgY6nsWdattgjJle+PVkSe+U9JqkxyStLXzaWkmPRlVk2gwOulc12tp8V5JdjNoCAAAUJ7gJgD2e8Zs4UWpspOMpSdVFfE6jpIeMMVVyQfURa+0PjDEbJD1ijPmUpE5JH46wzlQJTq6i4xmdYNTWWvcqEgAAAEYW3H1Ox9MP7vJ0xgye1tqXJV03wsePSlodRVFpx4m20aurk86dc/sVJk/2XQ0AAEByETz9ammRnn/edxX+lbTHE8UheEavrs6tjNsCAABcHqO2frW0SPv2ue14eUbwjMCuXVJVldtMjGgE19RwwBAAAMDl0fH0q7XVTeodOuS7Er8InhHYtUuaP18aP953JdkVdDwJngAAAJdH8PSLK1UcgmcEuMMzeozaAgAAFIfg6Vdrq1vzfsAQwTMCBM/oMWoLAABQHPZ4+jV/vlvpeCJUJ09K3d0Ez6gxagsAAFAcOp5+TZkizZ5Nx5PgGbLdu91K8IwWwRMAAKA4BE//WlroeBI8QxZcpdLW5reOrGOPJwAAQHEInv61ttLxJHiGjDs84zFhgjRpEh1PAACAsZw65W5b4MYFf1paXPAcGvJdiT8Ez5Dt2uW6cTNm+K4k++rqCJ4AAABj6euj2+lba6s0MCAdOeK7En8IniELTrQ1xncl2VdXx6gtAADAWAie/gV3eeZ53JbgGTKuUonPtGl0PAEAAMZC8PQvuMszzwcMETxDNDTkTrUleMaDUVsAAICxnTrFHZ6+0fEkeIbq0CE3u03wjAejtgAAAGOj4+nf1KlSfT0dT4SEE23jxagtAADA2AieyZD3K1UIniHavdutBM94MGoLAAAwNoJnMrS00PFESHbtcqfZBjPciFYQPK31XQkAAEByscczGYKOZ16fuxI8Q7Rrl9TcLE2Y4LuSfJg2TRocdK/iAQAAYGR0PJOhpcW9CHD0qO9K/CB4hmjXLqmtzXcV+VFX51bGbQEAAEZH8EyG4EqVvO7zJHiGiDs840XwBAAAGNvJk4zaJkGwHS+v+zwJniHp63PXqRA84zNtmlu5UgUAAGBkp09LZ84MP2+CP3Q8EYrglQuCZ3zoeAIAAFxe8DyJ4Onf9OnuPk+CJyrCHZ7xI3gCAABcXjAZFjxvgj/GuK4no7aoCMEzfozaAgAAXF7wPImOZzK0tNDxRIV273abthsafFeSH3Q8AQAALo9R22Sh44mKBVepGOO7kvyYOtWtBE8AAICRMWqbLC0t7s/k+HHflcSP4BmSPXu4wzNu48dLkyczagsAADAaOp7JElypksdxW4JnCKx1wTM4IhnxmTaNjicAAMBo6HgmS56vVCF4huD4cRd+CJ7xq6sjeAIAAIyGw4WSJeh45nGfJ8EzBMFfnOAvEuJTV8eoLQAAwGh6e93WpPHjfVcCyR1EOnkyHU+UKQiedDzjx6gtAADA6Hp6GLNNEmPye6UKwTMEwV8cgmf8GLUFAAAYXU8PY7ZJk9crVQieIdizR6qtlWbM8F1J/hA8AQAARtfbS/BMGjqeKFtwoi13eMZv2jT2eAIAAIyGUdvkaW2V3nhDOnXKdyXxIniGgKtU/Ak6ntb6rgQAACB5GLVNnrze5UnwDAHB05+6Ohc68/aKEQAAQDEYtU2eIDfkbZ8nwbNCx4+7V5IInn4EP0gZtwUAALgUo7bJQ8cTZeEqFb+CH6QcMAQAAHChwUHpxAk6nkkzd640YQIdT5Qo+AsTvHKBeBE8AQAARnbypFsJnskybpw0fz4dT5SIOzz9YtQWAABgZMHzI0Ztk6elhY4nSrRnj1RTI82c6buSfKLjCQAAMLLg+REdz+RpbaXjiRJxh6dfBE8AAICRBR1PgmfytLRIhw9Lp0/7riQ+BM8KcZWKX4zaAgAAjIxR2+QK8kNnp9cyYkXwrBDB06+pU91KxxMAAOBCjNomVx6vVCF4VuD4cfdG8PSnqsrtsSV4AgAAXIiOZ3IF+SFPBwwRPCvAibbJUFdH8AQAALgYezyT64orXAOFjud5jDHNxpifG2O2GWO2GmM+U/j4DGPMk8aYjsJaH325yRL8ReEOT7+mTWOPJwAAwMV6e92dkTU1vivBxaqrpeZmOp4XOyfpP1lrl0m6WdI9xpirJN0raZ21drGkdYX3cyX4i0LH0y86ngAAAJfq6XHPk7h9IZlaWuh4XsBae8ha+6vCr09I2iZpnqQ7JT1U+LSHJN0VVZFJtWePNGWKNGuW70ryjeAJAABwqZ4exmyTrLWVjueojDGtkq6T9IKkOdbaQ5ILp5Jmh11c0nGHZzIwagsAAHCp3l6CZ5K1tEgHD0pnz/quJB5FB09jTK2k/yfp31tri+4vGWPuNsa0G2Pau7u7y6kxsbhKJRnoeAIAAFwqGLVFMrW0SEND0v79viuJR1HB0xgzXi50ftNa+93Ch7uMMY2F32+UdGSkr7XW3m+tXWWtXdXQ0BBGzYlB8EwGgicAAMClGLVNtrxdqVLMqbZG0tckbbPW/tV5v/WYpLWFX6+V9Gj45SVXT4/05psEzySYNk06ccK9YgQAAACHUdtkmz/frZ2dfuuISzEdz1slfULSO4wxLxXe7pB0n6Q1xpgOSWsK7+cGd3gmR12dZK108qTvSgAAAJKDUdtka2py6759fuuIS/VYn2CtfVbSaMfnrA63nPQgeCZH8Ere8eP8cAUAAAjQ8Uy2SZOkhob8BM+STrXFsGAWu6XFaxmQNLtwnvKREXcZAwAA5M/p09KZMwTPpGtuJnhiDHv2SJMnu1cp4Fdjo1sPHfJbBwAAQFIEV80xDZZsBE+MiTs8k2PuXLcePuy3DgAAgKQITvyn45lsBE+MiatUkmPOHLfS8QQAAHCCjifBM9mam92f1YkTviuJHsGzTATP5JgwQZo5k44nAABAgFHbdGhuduv+/X7riAPBswy9vdKxYwTPJJk7l+AJAAAQYNQ2HYLgmYdxW4JnGbhKJXnmzmXUFgAAIEDHMx3ydJcnwbMMBM/kaWyk4wkAABBgj2c6zJvnDisleGJE3OGZPEHH01rflQAAAPgXjNrS8Uy28ePd81iCJ0a0Z480aZI0e7bvShBobJQGBoZf3QMAAMiznh535/z48b4rwVjycqUKwbMM3OGZPNzlCQAAMKynhzHbtCB4YlR79jBmmzRB8OSAIQAAADdqS/BMhyB4Zn3LGMGzDJ2dHCyUNI2NbqXjCQAA4Dqe7O9Mh+Zm6dQp6fhx35VEi+BZov5+qbtbmj/fdyU4Hx1PAACAYXQ80yMvd3kSPEvU2elWgmeyTJ8uTZxIxxMAAEBij2eaEDwxIoJnMhnjup4ETwAAAEZt04TgiREFwZPDhZInuMsTAAAg7xi1TY+5c6Xqamn/ft+VRIvgWaLOTmncOOmKK3xXgos1NtLxBAAAGByUTpwgeKZFVZXLFnQ8cYG9e91fDC7jTR46ngAAAC50SozapklTE8ETF+nsZH9nUjU2SkePSmfO+K4EAADAn95et9LxTI/gLs8sI3iWqLOT/Z1JFVypcuSI3zoAAAB86ulxK8EzPZqb3R5Pa31XEh2CZwmGhtwrEXQ8k4m7PAEAAIaDJ6O26dHcLJ0+Lb3xhu9KokPwLEFXlxvjJHgmU2OjWzlgCAAA5BmjtumThytVCJ4l4A7PZKPjCQAAQMczjQieuAB3eCbbnDlupeMJAADyjD2e6UPwxAXoeCbbhAnSzJkETwAAkG+M2qZPQ4N7LkvwhCR3h2ddHf+Ik4y7PAEAQN719EhVVdKUKb4rQbHGjcv+XZ4EzxJwh2fyNTbS8QQAAPnW3e2mwIzxXQlKEVypklUEzxJwh2fy0fEEAAB5d+CANG+e7ypQquZmOp4ooOOZfHPnuo5nli/fBQAAuJyDB6UrrvBdBUrV3OxeNBga8l1JNAieRTp1Sjp6lOCZdI2N0sDA8GluAAAAeXPwIB3PNGpqks6elbq6fFcSDYJnkTjRNh24yxMAAOTZmTPSkSN0PNMo61eqEDyLxB2e6dDY6FYOGAIAAHkUPAei45k+BE9IouOZFnQ8AQBAnh086FY6nulD8IQkd4dnVdVwRw3JFARPOp4AACCPDhxwK8EzfWbMkCZPJnjmXmenG1morvZdCS5n+nRp4kSCJwAAyKeg48mobfoYk+0rVQieReIOz3QwxnU9g1f7AAAA8uTAAWn8eGnmTN+VoBwET3CHZ4osWyZt3eq7CgAAgPgdPOi2ho3jWX4qETxzbnDQ/QUgeKbDihXSq6+648QBAADyhDs806252R2See6c70rCR/AswuHD7g+f4JkOK1e6y3e3bfNdCQAAQLwOHOBgoTRrbpaGhrJ5QwPBswjc4ZkuK1e69aWX/NYBAAAQt4MHCZ5pluUrVQieReAOz3RZvNgdRb15s+9KAAAA4nPypNTby6htmjU1uZXgmVN797o1eAUCyVZVJV1zDR1PAACQL8FVKnQ804uOZ851drr7IevqfFeCYq1c6YKntb4rAQAAiAd3eKbftGnS1KkEz9ziDs/0WbFCevNNaf9+35UAAADEg45nNmT1ShWCZxG4wzN9OGAIAADkzYEDbiV4phvBM8f27iV4ps0110jGEDwBAEB+HDwo1dayPSztchs8jTEPGGOOGGO2nPexGcaYJ40xHYW1Ptoy/entlY4fJ3imzdSp0sKFnGwLAADygzs8s6G5WerqkgYGfFcSrmI6nl+X9O6LPnavpHXW2sWS1hXez6Tg1Qb2eKZPcMAQAABAHnCHZzYEJ9sGo9NZMWbwtNY+I+nYRR++U9JDhV8/JOmukOtKDO7wTK+VK6WdO6UTJ3xXAgAAEL2DBznRNguC4Jm1QzLL3eM5x1p7SJIK6+zRPtEYc7cxpt0Y097d3V3mw/kT3OFJ8Eyf4IChl1/2WwcAAEDUrKXjmRVZvcsz8sOFrLX3W2tXWWtXNTQ0RP1woevslKqrpblzfVeCUq1Y4VbGbQEAQNYdO+b2BNLxTD+C54W6jDGNklRYj4RXUrJ0dro//Koq35WgVPPmSTNncsAQAADIPu7wzI4pU6QZMwiegcckrS38eq2kR8MpJ3mC4In0MYYDhgAAQD5wh2e2NDXlMHgaY74taYOkK40x+40xn5J0n6Q1xpgOSWsK72dSZyf7O9NsxQrplVekc+d8VwIAABCdoOPJqG02ZPEuz+qxPsFa+7FRfmt1yLUkzuCge/WIjmd6rVwpnT4tvf66tHy572oAAACiEQTPxka/dSAczc3S88/7riJckR8ulGZdXa5TRvBMr7e9za3f/a7fOgAAAKJ04IA722LiRN+VIAzNzdLRo1Jfn+9KwkPwvIygvU3wTK+WFukd75C+/nVpaMh3NQAAANHgDs9syeJdngTPyyB4ZsMnPynt2iX94he+KwEAAIjGgQMcLJQlWbxSheB5GQTPbPiN35Dq6qQHHvBdCQAAQDQOHiR4ZklTk1uD04qzgOB5GZ2dUk2NVF/vuxJUYsoU6SMfkb7zHenECd/VAAAAhOvcOXc2CaO22RH8WTJqmxP79rlupzG+K0GlPvlJtzn7kUd8VwIAABCuri53lgUdz+yYPNkdFkXwzIkgeCL9br5ZWrpUevBB35UAAACEq6PDra2tXstAyObNY9Q2Nwie2WGM63o+95y0fbvvagAAAMKzcaNbV63yWwfC1dRExzMXzpyRDh8meGbJJz4hVVW5q1UAAACyYuNG1+2cNct3JQgTwTMnDh6UrCV4Zkljo/Se90h/93fZ+kcMAADybeNG6cYbfVeBsDU1SUeOSAMDvisJB8FzFFylkk1/8Reum/2Rj0hnz/qudhJMXwAAEX9JREFUBgAAoDLd3dKePQTPLApOtj10yG8dYSF4joLgmU1Ll0pf/aq0fr30uc/5rgYAAKAy7e1uJXhmT3CXZ1Ym9QieoyB4ZtdHPyrdc4/0l38pfe97vqsBAAAo38aN7hDF66/3XQnCRvDMiX37pPp6qbbWdyWIwl/+pXtl8Hd+h1NuAQBAem3cKF15pVRX57sShC0Ytc3KlSoEz1F0dtLtzLKJE6VHHpGqq6WbbpL+4R/cYVIAAABpYS0HC2VZXZ1rgtHxzDju8My+1lb3w/qaa6Tf/m3pQx9yG/QBAADS4MABqauL4JlVxmTrShWC5ygInvmwYIH01FPSn/+59IMfSMuXS1/6Unb+gQMAgOzauNGtBM/smjcvO89LCZ4j6OuTjh4leOZFVZX0n/+zOxVuxQrpj/9YammR7rjDjeO+8YbvCgEAAC61caPbNrRype9KEJWmpuzs8az2XUASBa8qEDzz5ZprpCeflHbtkh58UHrgAXffpyRddZV0223SLbdIV1/trmWZPNlvvQAAIN+CLUOTJvmuBFFpapIOHpQGB12zJM0IniPgKpV8W7DAjdt+8YvSCy9Iv/iF9PTT0je+If3t37rPGTfOfd6SJa47On++W4O3uXPT/8MBAAAkl7VuWus3f9N3JYhSU5MLnV1d0hVX+K6mMgTPERA8IbnRlVtvdW/33iudOyd1dEhbt0pbtrh1507p+eelY8cu/Nrx490PiqYm90MieGtsvPD9qVP9/G8DAADptmOHdPw4+zuz7vwrVQieGRQEz+DSVkByQXTZMvf2oQ9d+HsnT7orePbuHV737nWjES++6A4uOnXq0u9ZWzt6KD3/4zU18fxvBAAA6cDBQvkQ5JH9+9P/Z03wHMG+fdKcOe6uR6AYtbVuH+hVV43+OSdOuCAavB06dOH7v/ylW/v7L/3aurrRQ+n5v2bfKQAA+bBxo/v//eXLfVeCKJ0fPNOO4DmCzk7GbBG+qVOlK690b6OxVurpGTmYBm/PPuvWM2cu/fr6+rE7qHPn8qIKAABp98IL0nXXuYksZNesWdKECQTPzNq37/LhAIiKMdL06e7tct1Ta6U33xw5mAZvr7/u1nPnLv36mTMvDKPNzZe+sf8UAIBk2r5d2rBB+pM/8V0JomaM2+eZhStVCJ4j2LdPeuc7fVcBjM4YacYM93b11aN/3tCQu5N2pGAadFRfflk6fNiF2fNNmzZyIA3empoY7QUAwIe//mvXBfv93/ddCeLQ1ETHM5N6etxePEZtkQXjxkkNDe5txYrRP+/MGRdC9+0b+W3jRumNNy79ulmzLh9O581zJ/wCAIBw9PRIX/+69NGPujNJkH3z5g0fJpVmBM+LcJUK8mjCBKm11b2Npr/fvdo2UjDdtcvdddrTc+HXGOP2lAZBtLHR/Z/k7NnDb8H7tbXu8wEAwOgeeMCdpv+Zz/iuBHFpapK+9z03nZbm50oEz4sQPIGRTZ4sLV7s3kZz4sToXdMtW6Sf/vTScBqYNGnkUNrQ4EaK6+vd2/Tpw7+uqUn3D2AAAEoxOOjGbN/6Vun6631Xg7g0NUkDA+7e+JkzfVdTPoLnRQieQPmmTh37WpmBAam7W+rqko4cufAt+NjBg9JLL7lfnz07+veqrh4OoucH0uD9qVOH32prL1zP//XEiQRY4P+3d+8xdpRlHMd/z+52uy0UpLa1dZcqJlzURLk0BZEoxkugCbb+YYIBRSwh/aMgJBpKpITLHwQSSTBBCSIB0UBIAG2aEkRFSCQl1IYWsFAKlO7SAlsordDbdvv4xzuHnZ7bzrnMmTnnfD/Jm5nzzsw575lnZ+Y8O+95D4D8W71aevNN6bbbsm4JWin+kyoknh1keDh8L27evKxbAnSmqVPDCbRwEq3GXfrwwzCCb2E62fzWrROPy43oW05f35GJ6VFHSdOnh7u88Wml+Wp1AwPhPQ8MhC7NJLgAgHrdcYc0f760ZEnWLUErDQ6G6chI9TE78o7Es8jwcAguv4kEZM9s4g5mPQ4cCN1/P/ooTJPO790byq5dYfjyffsm6vbtK/8bqkn194dENF4KyWmlUm35wEAYwGnKlPDchfnix9WWFT8mOQaA/Nm4UXrqKenWW/mc2m0K/6xv959U4c+2yLZtdLMFOkUhOZs1q7nPOz5emozGp8V1Bw5ULvv3l9bt3l19ebXux83Q15csSe3rKy29vc2pb8Y2vb3lS09P5WXF65CEA8iD/fulK68MPWkuuyzr1qDV5s4N16V2/0kVEs8iw8PSGWdk3QoAedbbG7rkHn10Nq9/+HC461pISsfGJsrBg+XnJ3tcz7rj46E786FDoS2F+Xh9cam0rPh3ZPPCrPHktRkJcCufI560FyfwSZdVW7enJ+uoAu1lfFy6+OIwevyf/hQG3EN36esLySeJZwdxDwGl3zyAPOvpCV1sBwaybknzHD5cPimtJ4mN14+Pl5bCa1Uqky1vxnOMjaXTjnaRdnKbdFnh7n7xXf5ydUnWSVLX18eddCTnLi1fLj3yiHT77dJFF2XdImRlaIjEs6Ps3Bn+a09XWwBorZ6eUKZMybol7a04Ea0nuS1O2Ks9TmNZrc9z4ED9r1/4B0Xa3deLFbqDx5PTwlcDyn0PvFJ90rqpU8sPfjZtWijchc6vG2+U7rpLuuYa6eqrs24NsjQ0JL3yStataAyJZww/pQIAaGck8PVxn7gTPTY2kYzGS5p1Bw+GUvged3x+167SuuL1GjV1auWktFzCWhj5Oz4tVxefMqp3bTZvllaulB5+WLr0UumWW7JuEbI2OBh+D72dkXjGkHgCANB9zCbuQk6blnVrauMektdyCWl8kLJ9+0oHQqtUF182Olo6cNrHH9fetbu3t3pimiR5rTadNq0zEttt26SbbpLuuy98nWLlSun66zvjvaExQ0PSnj1h9P0ZM7JuTX1IPGNIPAEAQDsxC3cT+/tb92HUPSS3e/eGJLTR6QcflNbV87NV9SatSRPh3t7m78edO6W1a8PAQU8/La1fH/4BcsUV0rXXSnPmNPc10b7iP6lyyinZtqVeJJ4xw8PhxD17dtYtAQAAyCezie+O1vs7y5M5dGjiLmstSWy5utFRaevWI+v37q29TYXvyibpVlzo9t7TE+5Ix+86j46G5GH79lBfeO4zz5Suu05aulSaP7+puxMdoJB4joyQeHaE4eFwt5Mv2QMAAGSnr0865phQ0nD4cEj66k1m49Pdu6UdOybWHRsLdzMLg3dNmRK6AhdGI581KySZg4OhnHZaeNxJI5Wj+QYHw7SdR7Yl8YwpJJ4AAADoXD094e7k9OlZtwRIphMST+7txWzbRuIJAAAAIF8Kd8vffjvrltSPxDMyPh4CSeIJAAAAIG8GB7nj2RHeeScknySeAAAAAPJmaIjEsyPwUyoAAAAA8mpoqIu72prZeWb2qpltMbMVzWpUFk46SXrsMWnhwqxbAgAAAABHWrZMeuCBrFtRv7pHtTWzXkl3SvqupBFJz5vZKnf/b7Ma10ozZ0pLlmTdCgAAAAAodeqpWbegMY3c8VwoaYu7v+HuByU9JGlxc5oFAAAAAOgUjSSeg5KGY49HojoAAAAAAD7RSOJpZeq8ZCWzy81snZmtGx0dbeDlAAAAAADtqJHEc0RSfAzYIUnbi1dy97vdfYG7L5g9e3YDLwcAAAAAaEeNJJ7PSzrRzE4ws35JF0pa1ZxmAQAAAAA6Rd2j2rr7ITNbLukJSb2S7nX3l5vWMgAAAABAR6g78ZQkd18jaU2T2gIAAAAA6ECNdLUFAAAAAGBSJJ4AAAAAgFSReAIAAAAAUkXiCQAAAABIFYknAAAAACBVJJ4AAAAAgFSReAIAAAAAUkXiCQAAAABIFYknAAAAACBVJJ4AAAAAgFSReAIAAAAAUkXiCQAAAABIFYknAAAAACBV5u6tezGzUUlvtewF6zNL0s6sG4EjEJP8ISb5RFzyh5jkE3HJH2KST8Qlf9ohJp9z99nFlS1NPNuBma1z9wVZtwMTiEn+EJN8Ii75Q0zyibjkDzHJJ+KSP+0cE7raAgAAAABSReIJAAAAAEgViWepu7NuAEoQk/whJvlEXPKHmOQTcckfYpJPxCV/2jYmfMcTAAAAAJAq7ngCAAAAAFLVdYmnmf3QzF42s8NmVnFEKDM7z8xeNbMtZrYiVj/TzJ40s9ei6XGtaXlnS7JfzexkM3shVvaY2VXRshvM7O3YskWtfxedJenfupltNbMXo/2+rtbtUZuEx8rxZvaUmW2Kznc/jy3jWGmSSteJ2HIzs99Eyzea2elJt0V9EsTkoigWG83sWTP7amxZ2XMZGpcgLuea2e7Yeen6pNuiPgli8stYPF4ys3Ezmxkt41hJgZnda2bvmdlLFZa3/zXF3buqSPqipJMl/UvSggrr9Ep6XdIXJPVL2iDpS9Gy2yStiOZXSLo16/fUCaXW/RrF6B2F3wmSpBsk/SLr99FJJWlMJG2VNKvRmFKaFxdJ8ySdHs3PkLQ5dg7jWGlOHCpeJ2LrLJL0uCSTdJak55JuS0ktJmdLOi6aP78Qk+hx2XMZpSVxOVfS6nq2paQTk6L1L5D0z9hjjpV04vINSadLeqnC8ra/pnTdHU933+Tur06y2kJJW9z9DXc/KOkhSYujZYsl3R/N3y9pSTot7Tq17tdvS3rd3d9KtVXdrdG/dY6VdEy6X919h7uvj+b/J2mTpMGWtbA7VLtOFCyW9EcP1kr6lJnNS7gtajfpfnX3Z919V/RwraShFrexGzXy986xko5a9+uPJD3YkpZ1MXd/RtIHVVZp+2tK1yWeCQ1KGo49HtHEh7bPuPsOKXy4kzSnxW3rVLXu1wtVehJcHnU9uJdunU2RNCYu6W9m9h8zu7yO7VGbmvarmX1e0mmSnotVc6w0rtp1YrJ1kmyL2tW6X5cq3D0oqHQuQ2OSxuVrZrbBzB43sy/XuC1qk3i/mtl0SedJeiRWzbGSjba/pvRl3YA0mNnfJc0ts+hX7v7XJE9Rpo7hfxtULS41Pk+/pO9LujZW/TtJNyvE6WZJv5b0s/pa2j2aFJOvu/t2M5sj6UkzeyX6rx3q1MRj5WiFDwtXufueqJpjpTmSXCcqrcM1Jh2J96uZfUsh8TwnVs25LB1J4rJe4aszH0XfO/+LpBMTbova1bJfL5D0b3eP34njWMlG219TOjLxdPfvNPgUI5KOjz0ekrQ9mn/XzOa5+47o9vZ7Db5W16gWFzOrZb+eL2m9u78be+5P5s3s95JWN6PNna4ZMXH37dH0PTN7TKHLxzPiWKlbM+JiZlMUks4/u/ujsefmWGmOateJydbpT7AtapckJjKzr0i6R9L57v5+ob7KuQyNmTQusX+Myd3XmNlvzWxWkm1Rl1r2a0kPM46VzLT9NYWutuU9L+lEMzshurt2oaRV0bJVki6J5i+RlOQOKiZXy34t+a5B9AG84AeSyo4IhppMGhMzO8rMZhTmJX1PE/ueYyUdSeJikv4gaZO73160jGOlOapdJwpWSfpJNBLhWZJ2R92jk2yL2k26X81svqRHJf3Y3TfH6qudy9CYJHGZG523ZGYLFT6fvp9kW9Ql0X41s2MlfVOx6wzHSqba/5qS9ehGrS4KH7RGJB2Q9K6kJ6L6z0paE1tvkcJIkK8rdNEt1H9a0j8kvRZNZ2b9njqhVNqvZeIyXeFidGzR9g9IelHSRoWDbV7W76ndS5KYKIygtiEqL3Os5CYu5yh0s9ko6YWoLIqWcaw0LxYl1wlJyyQti+ZN0p3R8hcVG0m90jWGknpM7pG0K3ZcrIvqK57LKC2Jy/Jov29QGPTp7GrbUtKPSfT4p5IeKtqOYyW9mDwoaYekMYVcZWmnXVMsaiwAAAAAAKmgqy0AAAAAIFUkngAAAACAVJF4AgAAAABSReIJAAAAAEgViScAAAAAIFUkngAAAACAVJF4AgAAAABSReIJAAAAAEjV/wFOAKgex22SuQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 1152x648 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "tds.U2 = np.load('newton_psaap_Np250.npy')\n",
-    "\n",
-    "ne = tds.U2[0:tds.Np]\n",
-    "ni = tds.U2[tds.Np:2*tds.Np]\n",
-    "nT = tds.U2[2*tds.Np:]\n",
-    "Te = nT/ne\n",
-    "\n",
-    "plt.figure(figsize=(16,9))\n",
-    "plt.plot(tds.xp, Te, 'b-')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/psaapProperties.py b/psaapProperties.py
deleted file mode 100644
index 549ce9335..000000000
--- a/psaapProperties.py
+++ /dev/null
@@ -1,233 +0,0 @@
-import numpy as np
-from scipy.interpolate import CubicSpline
-
-
-class Reaction(object):
-    def __init__(self, *initial_data, **kwargs):
-        for dictionary in initial_data:
-            for key in dictionary:
-                setattr(self, key, dictionary[key])
-        for key in kwargs:
-            setattr(self, key, kwargs[key])
-
-def setPsaapProperties(gam, inputV0, inputVDC, params, Nr, iSample):
-    """Sets non-dimensional properties corresponding to Liu 2014 paper.
-
-    Inputs:
-      gam : Secondary electron emission coefficient
-      params : chebSolver.modelParams class
-
-    Outputs: None
-      params data is overwritten using values from Liu 2014.
-    """
-    ###################################################################
-    # User specified parameters (you may change these if you wish to
-    # run a different scenario from Liu 2014)
-    ###################################################################
-
-    # densities
-    nAr = 3.22e22     # background number density of Ar [1/m^3] (corresponds to p=1Torr)
-    np0 = 8e16        # "nominal" electron density [1/m^3]
-
-    # masses
-    # me = 9.10938356e-31        # mass of an electron [kg]
-    # me = 5.489e-4              # mass of an electron [u]
-    me = 0.511e6                 # mass of an electron [eV/c2]
-    # mAr = 39.948               # mass of an argon atom [u]
-    # mAr = 39.948 * 1.66054e-27 # mass of an argon atom [kg]
-    mAr = 37.2158e9              # mass of an electron [eV/c2]
-    # u = 931.4941e6             # eV/c2
-    c = 299792458                # speed of light [m/s]
-    se = 40                      # momentum cross section [m^2]
-
-    # nominal electron energy
-    e0 = 1.0  # [eV]
-
-    # pressure
-    p = 133.3224 * 1.5  # [J/m^3] *1.5 to convert it to energy
-
-    # gas energy at the wall
-    Tg0 = 0.038778    # 3/2*300K*kB ~ (p0 - nT[:,0])/ntot
-
-    # characteristics of driving voltage
-    V0            = inputV0       # amplitude of driving voltage [V]
-    verticalShift = inputVDC      # DC voltage (vertical shift in driving voltage)
-    tau           = (1./13.6e6)   # period of driving voltage [s]
-    L             = 2.00*0.005    # half-gap-width [m] (gap width is 2 cm)
-    electrodeArea = np.pi*0.05**2 # electrode area [m^2] (electrode diameter = 0.1 m)
-
-    # transport parameters
-    nmue   = 9.66e21   # argon number density times electron mobility [1/(V*cm*s)]
-    nmui   = 4.65e19   # argon number density times ion mobility [1/(V*cm*s)]
-    nDe    = 3.86e22   # argon number density times electron diffusivity [1/(cm*s)]
-    nDi    = 2.07e18   # argon number density times ion diffusivity [1/(cm*s)]
-
-    # reaction parameters (NB: k_i = Ck*exp(-A/Te))
-    Ck = 1.235e-7    # ionization rate pre-exponential factor [cm^3/s]
-    A  = 18.687      # activation temperature [eV]
-    dH = 15.7        # energy lost per electron due to ionization rxn [eV]
-
-    # BC parameters
-    # ks = 1.19e7            # electron recombination rate [cm/s]
-    ks = 1.366109824889323e7 # electron recombination rate [cm/s/eV]
-
-    ###################################################################
-    # Constants of nature (probably shouldn't change unless you have
-    # root privileges on universe)
-    ###################################################################
-    qe   = 1.6e-19   # unit charge [C]
-    eps0 = 8.86e-12  # permittivity of free space [F/m]
-    kB   = 1.38e-23  # Boltzmann constant [J/K]
-    # kB   = 8.62e−5 # Boltzmann constant [eV/K]
-
-
-    ###################################################################
-    # Calculate non-dimensional parameters
-    ###################################################################
-
-    # 1) Convert input units to base SI (except eV)
-    nDe  *= 100. # 1/(m*s)
-    nDi  *= 100. # 1/(m*s)
-    nmue *= 100. # 1/(V*m*s)
-    nmui *= 100. # 1/(V*m*s)
-    Ck   *= 1e-6 # m^3/s
-    ks   *= 0.01 # m/s
-    se   *= 1.0e-20  # m^2
-
-    # 2) Compute "raw" transport parameters
-    De  = nDe/nAr
-    Di  = nDi/nAr
-    mue = nmue/nAr
-    mui = nmui/nAr
-
-    # 3) Compute non-dimensional properties required by solver
-    De     = De*tau/(L*L)
-    Di     = Di*tau/(L*L)
-    mue    = mue*V0*tau/(L*L)
-    mui    = mui*V0*tau/(L*L)
-    Ck     = Ck*tau*nAr
-    A      = A*1.5/e0  # 1.5 to convert from temperature to energy
-    dH     = dH/e0
-    dEps   = np.array([0.0,15.7,0.0])
-    qStar  = V0/e0 # qe*V0/e0, since e0 in eV, need qe*V0 in eV, which is just V0 in V
-    alpha  = qe*np0*L*L/(V0*eps0)
-    ks     = ks*tau/L
-    p0     = p/qe/np0
-    kappaB = 4.878171165833662*1.6129 # non-dimensional thermal conductivity of background specie
-                                      # (2/3)*tau/L**2*Kb/np0/kB,
-                                      # where Kb is the thermal conductivity of background specie
-
-    params.beta = np.array([[2],[1],[0]], dtype=np.int64)
-    params.alfa = np.array([[1],[0],[1]], dtype=np.int64)
-
-    # 4) Set values in params class
-    params.D[0]    = De
-    params.D[1]    = Di
-    params.mu[0]   = mue
-    params.mu[1]   = mui
-    params.A[0]    = Ck
-    params.B[0]    = 0.0
-    params.C[0]    = A
-    params.dH[0]   = dH
-    params.dEps[:] = dEps[:]
-    params.qStar   = qStar
-    params.alpha   = alpha
-    params.ks      = ks
-    params.gam     = gam
-    params.kappaB  = kappaB
-    params.nAronp0 = nAr / np0
-    params.p0      = p0
-    params.Tg0     = Tg0
-    params.EC      = 2.0 * me / mAr \
-        * np.sqrt(16.0 * (me + mAr) * e0 * c**2
-                  / (3.0 * np.pi * me * mAr)) * se * nAr * tau
-    # params.EC = 2.0 * me / mAr * 3.8e9 * tau
-
-    params.verticalShift = verticalShift / V0
-
-    # Parameters needed to compute the current with dimensions
-    params.V0Ltau  = V0 / (L * tau)
-    params.V0L     = V0 / L
-    params.LLV0tau = (L*L) / (V0*tau)
-    params.tauL    = L / tau
-    params.np0     = np0           # "nominal" electron density [1/m^3]
-    params.qe      = qe            # unit charge [C]
-    params.eps0    = eps0          # unit charge [C]
-    params.eArea   = electrodeArea # electrode area [m^2]
-
-    reactionExpressionslist = [f"{params.A[0]} * energy**{params.B[0]} * np.exp(-{params.C[0]} / energy)"]
-
-    reactionTExpressionslist = [f"{params.A[0]} * (energy**({params.B[0]}-1)) * np.exp(-{params.C[0]}/energy) * ({params.B[0]} + {params.C[0]}/energy)"]
-
-    reactionExpressionTypelist = [False]
-
-    reactionsList = []
-    for i in range(Nr):
-        if reactionExpressionTypelist[i]:
-            Nsample = 72
-            N300 = 200
-
-            rateCoeff = np.fromfile('./BOLSIGChemistry/reaction300K_%s.dat' %str(i))
-            rateCoeff = np.reshape(rateCoeff,[Nsample, N300]).T[:,iSample]
-
-            Te = np.fromfile('./BOLSIGChemistry/reaction300K.Te.dat')
-            Te = np.reshape(Te,[Nsample, N300]).T[:,iSample]
-
-            # Nondimensionalization of mean energy and transformation to log scale.
-            # Te *= 1.5
-            TeLog = np.log(Te)
-
-            # Find first non-zero value of the coefficient rate.
-            I = np.nonzero(rateCoeff)
-
-            # Compute the slope of the rate coefficient between its first two non-zero values.
-            # Finite differences are used.
-            dydx = (rateCoeff[I[0][0] + 1] - rateCoeff[I[0][0]]) \
-                 / (Te[I[0][0] + 1] - Te[I[0][0]])
-
-            # Arrhenius form: kf = A * exp(-C / Te)
-            # C = (dkf/dTe) / kf * Te**2.0
-            # A = kf / exp(-C / Te)
-            C = Te[I[0][0]]**2.0*dydx / rateCoeff[I[0][0]]
-            # A = rateCoeff[I[0][0]] / np.exp(-C/Te[I[0][0]])
-
-            # Compute pre-exponential coefficient, A, in log scale.
-            ALog = np.log(rateCoeff[I[0][0]]) + C / Te[I[0][0]]
-
-            # Transform rate coefficient in log scale.
-            rateCoeffLog = np.zeros(rateCoeff.shape)
-            rateCoeffLog[I[0][0]:] = np.log(rateCoeff[I[0][0]:])
-            # For the troublesome values, we use the Arrhenius form.
-            rateCoeffLog[0:I[0][0]] = ALog - C / Te[0:I[0][0]]
-            # Nondimensionalization in log scale.
-            if i < 2:
-                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
-            else:
-                rateCoeffLog += - np.log(1.0/tau) + np.log(np0)
-            # Nondimensionalization of the original rate, used for the plot and comparison.
-            rateCoeff *= tau * nAr
-
-            # Interpolation in log scale.
-            reactionExpressionsLog = CubicSpline(TeLog, rateCoeffLog)
-            # Gradient in log scale
-            reactionTExpressionsLog = CubicSpline.derivative(reactionExpressionsLog)
-
-            reaction = Reaction(rxnAlfa = params.alfa, rxnBeta = params.beta,
-                                rxnBolsig = reactionExpressionTypelist[i],
-                                kf_log = reactionExpressionsLog,
-                                kf_T_log = reactionTExpressionsLog)
-            reactionsList.append(reaction)
-
-        else:
-            rxn   = eval("lambda energy :" + reactionExpressionslist[i])
-            rxn_T = eval("lambda energy :" + reactionTExpressionslist[i])
-
-            reaction = Reaction(rxnAlfa = params.alfa, rxnBeta = params.beta,
-                                rxnBolsig = reactionExpressionTypelist[i],
-                                kf = rxn, kf_T = rxn_T)
-            reactionsList.append(reaction)
-
-    params.reactionsList = reactionsList
-
-    # 5) Dump to screen
-    params.print()
diff --git a/psaapPropertiesCurrentTestCase.py b/psaapPropertiesCurrentTestCase.py
deleted file mode 100644
index b372b994b..000000000
--- a/psaapPropertiesCurrentTestCase.py
+++ /dev/null
@@ -1,235 +0,0 @@
-import numpy as np
-
-
-class Reaction(object):
-    def __init__(self, *initial_data, **kwargs):
-        for dictionary in initial_data:
-            for key in dictionary:
-                setattr(self, key, dictionary[key])
-        for key in kwargs:
-            setattr(self, key, kwargs[key])
-
-def setPsaapPropertiesCurrentTestCase(gam, inputV0, inputVDC, params, Nr, iSample):
-    """Sets non-dimensional properties corresponding to Liu 2014 paper.
-
-    Inputs:
-      gam : Secondary electron emission coefficient
-      params : chebSolver.modelParams class
-
-    Outputs: None
-      params data is overwritten using values from Liu 2014.
-    """
-    ###################################################################
-    # User specified parameters (you may change these if you wish to
-    # run a different scenario from Liu 2014)
-    ###################################################################
-
-    # densities
-    nAr = 1.6102e21   # background number density of Ar [1/m^3] (corresponds to p=100 mTorr)
-    np0 = 8e16        # "nominal" electron density [1/m^3]
-
-    # masses
-    # me = 9.10938356e-31        # mass of an electron [kg]
-    # me = 5.489e-4              # mass of an electron [u]
-    me = 0.511e6                 # mass of an electron [eV/c2]
-    # mAr = 39.948               # mass of an argon atom [u]
-    # mAr = 39.948 * 1.66054e-27 # mass of an argon atom [kg]
-    mAr = 37.2158e9              # mass of an electron [eV/c2]
-    # u = 931.4941e6             # eV/c2
-    c = 299792458                # speed of light [m/s]
-    se = 40                      # momentum cross section [m^2]
-
-    # nominal electron energy
-    e0 = 1.0  # [eV]
-
-    # pressure
-    # p  = 133.3224*1.5      # [J/m^3] *1.5 to convert it to energy (1 Torr)
-    p  = 1 * 6.6661185 * 1.5 # [J/m^3] *1.5 to convert it to energy (50 mTorr)
-
-    # gas energy at the wall
-    Tg0 = 0.038778    # 3/2*300K*kB ~ (p0 - nT[:,0])/ntot
-
-    # characteristics of driving voltage
-    V0  = inputV0                 # amplitude of driving voltage [V]
-    verticalShift = inputVDC      # DC voltage (vertical shift in driving voltage)
-    # tau = (1./13.6e6)           # period of driving voltage [s]
-    tau = (1./27.1e6)             # period of driving voltage [s]
-    L   = 2.00*0.005              #1*0.005  # half-gap-width [m] (gap width is 1cm)
-    electrodeArea = np.pi*0.05**2 # electrode area [m^2] (electrode diameter = 0.1 m)
-
-    # transport parameters
-    nmue = 4.83e20   # argon number density times electron mobility [1/(V*cm*s)]
-    nmui = 2.33e18   # argon number density times ion mobility [1/(V*cm*s)]
-    nmum = 1 / (np.sqrt(16.0 * (mAr + mAr) * 300 * 8.62e-5 * c**2
-                        / (3.0 * np.pi * mAr * mAr)) * se * mAr * 1.6e-19 / c**2)
-    nDe  = 1.93e21   # argon number density times electron diffusivity [1/(cm*s)]
-    nDi  = 1.04e17   # argon number density times ion diffusivity [1/(cm*s)]
-    nDm  = 1.21e17   # argon number density times metastable diffusivity [1/(cm*s)]
-
-    # reaction parameters (NB: k_i = Ck*exp(-A/Te))
-    #Ck = np.array([1.235e-7,3.712e-8,2.05e-7,1.818e-9,2e-7]) # pre-exponential factors [cm^3/s]
-    #A  = np.array([18.687,15.06,4.95,2.14,0.0])      # activation temperature [eV]
-    #dH = np.array([15.7,11.56,4.14,-11.56,0.0])        # energy lost per electron due to ionization rxn [eV]
-
-    #Ck = np.array([1.235e-7,3.712e-8,2.05e-7,1.818e-9]) # pre-exponential factors [cm^3/s]
-    #A  = np.array([18.687,15.06,4.95,2.14])      # activation temperature [eV]
-    #dH = np.array([15.7,11.56,4.14,-11.56])        # energy lost per electron due to ionization rxn [eV]
-
-    #Ck = np.array([1.235e-7,0.0,0.0,0.0,0.0]) # pre-exponential factors [cm^3/s]
-    #A  = np.array([18.687,15.06,4.95,2.14,0.0])      # activation temperature [eV]
-    #dH = np.array([15.7,0.0,0.0,0.0,0.0])        # energy lost per electron due to ionization rxn [eV]
-
-    # nominal
-    Ck = np.array([1.235e-7,3.712e-8,2.05e-7,1.818e-9,2e-7,6.2e-10,3.0e-15,1.1e-31]) # pre-exponential factors [cm^3/s]
-
-    # slow excitation rate
-    #Ck = np.array([1.235e-7,0.5*3.712e-8,2.05e-7,1.818e-9,2e-7,6.2e-10,3.0e-15,1.1e-31]) # pre-exponential factors [cm^3/s]
-
-    ## fast excitation rate
-    #Ck = np.array([1.235e-7,2.0*3.712e-8,2.05e-7,1.818e-9,2e-7,6.2e-10,3.0e-15,1.1e-31]) # pre-exponential factors [cm^3/s]
-
-    A  = np.array([18.687,15.06,4.95,2.14,0.0,0.0,0.0,0.0]) # activation temperature [eV]
-    dH = np.array([15.7,11.56,4.14,-11.56,0.0,0.0,0.0,0.0]) # energy lost per electron due to ionization rxn [eV]
-    dEps = np.array([0.0,15.7,11.56,0.0])
-
-    # BC parameters
-    # ks = 1.19e7  # electron recombination rate [cm/s]
-    ks = 1.366109824889323e7 # electron recombination rate [cm/s/eV]
-
-    ###################################################################
-    # Constants of nature (probably shouldn't change unless you have
-    # root privileges on universe)
-    ###################################################################
-    qe   = 1.6e-19   # unit charge [C]
-    eps0 = 8.86e-12  # permittivity of free space [F/m]
-    kB   = 1.38e-23  # Boltzmann constant [J/K]
-    # kB   = 8.62e−5 # Boltzmann constant [eV/K]
-
-
-    ###################################################################
-    # Calculate non-dimensional parameters
-    ###################################################################
-
-    # 1) Convert input units to base SI (except eV)
-    nDe  *= 100. # 1/(m*s)
-    nDi  *= 100. # 1/(m*s)
-    nDm  *= 100.
-    nmue *= 100. # 1/(V*m*s)
-    nmui *= 100. # 1/(V*m*s)
-    Ck   *= 1e-6 # m^3/s
-    Ck[7] *= 1e-6 # Ck[7] is now in m^6/s
-    ks   *= 0.01 # m/s
-    se   *= 1.0e-20  # m^2
-
-    # 2) Compute "raw" transport parameters
-    De  = nDe/nAr
-    Di  = nDi/nAr
-    Dm  = nDm/nAr
-
-    mue = nmue/nAr
-    mui = nmui/nAr
-    mum = nmum/nAr
-
-    # 3) Compute non-dimensional properties required by solver
-    De    = De*tau/(L*L)
-    Di    = Di*tau/(L*L)
-    Dm    = Dm*tau/(L*L)
-
-    mue   = mue*V0*tau/(L*L)
-    mui   = mui*V0*tau/(L*L)
-    mum   = mum*V0*tau/(L*L)
-
-    Ck[0:2] = Ck[0:2]*tau*nAr
-    #Ck[2:5] = Ck[2:5]*tau*np0
-    Ck[2:6] = Ck[2:6]*tau*np0
-    Ck[6]  *= tau*nAr
-    Ck[7]  *= tau*nAr*nAr
-    A       = A*1.5/e0  # 1.5 to convert from temperature to energy
-    dH      = dH/e0
-    qStar   = V0/e0 # qe*V0/e0, since e0 in eV, need qe*V0 in eV, which is just V0 in V
-    alpha   = qe*np0*L*L/(V0*eps0)
-    ks      = ks*tau/L
-    p0      = p/qe/np0
-    kappaB  = 4.878171165833662*1.6129 # non-dimensional thermal conductivity of background specie
-                        # (2/3)*tau/L**2*Kb/np0/kB,
-                        # where Kb is the thermal conductivity of background specie
-
-    #params.beta = np.array([[2,2,2,1,1],[1,0,1,0,0],[0,1,0,0,0],[0,0,0,1,1]])
-    #params.alfa = np.array([[1,1,1,1,1],[0,0,0,0,0],[0,0,1,1,1],[1,1,0,0,0]])
-    #params.beta = np.array([[2,1,2,1],[1,0,1,0],[0,1,0,0],[0,0,0,1]], dtype=np.int)
-    #params.alfa = np.array([[1,1,1,1],[0,0,0,0],[0,0,1,1],[1,1,0,0]], dtype=np.int)
-    params.beta = np.array([[2,1,2,1,1,1,0,0],[1,0,1,0,0,1,0,0],[0,1,0,0,0,0,0,0],[0,0,0,1,0,1,2,1]], dtype=np.int64)
-    params.alfa = np.array([[1,1,1,1,1,0,0,0],[0,0,0,0,0,0,0,0],[0,0,1,1,1,2,1,1],[1,1,0,0,0,0,1,2]], dtype=np.int64)
-
-    # 4) Set values in params class
-    params.D[0]    = De
-    params.D[1]    = Di
-    params.D[2]    = Dm
-
-    params.mu[0]   = mue
-    params.mu[1]   = mui
-    params.mu[2]   = mum
-
-    params.A[:]    = Ck[:]
-    params.B[:]    = 0.0
-    params.C[:]    = A[:]
-
-    params.dH[:]   = dH[:]
-    params.dEps[:] = dEps[:]
-    params.qStar   = qStar
-    params.alpha   = alpha
-    params.ks      = ks
-    params.gam     = gam
-    params.kappaB  = kappaB
-    params.nAronp0 = nAr / np0
-    params.p0      = p0
-    params.Tg0     = Tg0
-    params.EC      = 2.0 * me / mAr \
-        * np.sqrt(16.0 * (me + mAr) * e0 * c**2
-                  / (3.0 * np.pi * me * mAr)) * se * nAr * tau
-    # params.EC = 2.0 * me / mAr * 3.8e9 * tau
-
-    params.verticalShift = verticalShift / V0
-
-    # Parameters needed to compute the current with dimensions
-    params.V0Ltau  = V0 / (L * tau)
-    params.V0L     = V0 / L
-    params.LLV0tau = (L*L) / (V0*tau)
-    params.tauL    = L / tau
-    params.np0     = np0           # "nominal" electron density [1/m^3]
-    params.qe      = qe            # unit charge [C]
-    params.eps0    = eps0          # unit charge [C]
-    params.eArea   = electrodeArea # electrode area [m^2]
-
-    reactionExpressionslist = [f"{params.A[0]} * energy**{params.B[0]} * np.exp(-{params.C[0]} / energy)",
-                               f"{params.A[1]} * energy**{params.B[1]} * np.exp(-{params.C[1]} / energy)",
-                               f"{params.A[2]} * energy**{params.B[2]} * np.exp(-{params.C[2]} / energy)",
-                               f"{params.A[3]} * energy**{params.B[3]} * np.exp(-{params.C[3]} / energy)",
-                               f"{params.A[4]} * energy**{params.B[4]} * np.exp(-{params.C[4]} / energy)",
-                               f"{params.A[5]} * energy**{params.B[5]} * np.exp(-{params.C[5]} / energy)",
-                               f"{params.A[6]} * energy**{params.B[6]} * np.exp(-{params.C[6]} / energy)",
-                               f"{params.A[7]} * energy**{params.B[7]} * np.exp(-{params.C[7]} / energy)"]
-
-    reactionTExpressionslist = [f"{params.A[0]} * (energy**({params.B[0]}-1)) * np.exp(-{params.C[0]}/energy) * ({params.B[0]} + {params.C[0]}/energy)",
-                                f"{params.A[1]} * (energy**({params.B[1]}-1)) * np.exp(-{params.C[1]}/energy) * ({params.B[1]} + {params.C[1]}/energy)",
-                                f"{params.A[2]} * (energy**({params.B[2]}-1)) * np.exp(-{params.C[2]}/energy) * ({params.B[2]} + {params.C[2]}/energy)",
-                                f"{params.A[3]} * (energy**({params.B[3]}-1)) * np.exp(-{params.C[3]}/energy) * ({params.B[3]} + {params.C[3]}/energy)",
-                                f"{params.A[4]} * (energy**({params.B[4]}-1)) * np.exp(-{params.C[4]}/energy) * ({params.B[4]} + {params.C[4]}/energy)",
-                                f"{params.A[5]} * (energy**({params.B[5]}-1)) * np.exp(-{params.C[5]}/energy) * ({params.B[5]} + {params.C[5]}/energy)",
-                                f"{params.A[6]} * (energy**({params.B[6]}-1)) * np.exp(-{params.C[6]}/energy) * ({params.B[6]} + {params.C[6]}/energy)",
-                                f"{params.A[7]} * (energy**({params.B[7]}-1)) * np.exp(-{params.C[7]}/energy) * ({params.B[7]} + {params.C[7]}/energy)"]
-
-    reactionsList = []
-    for i in range(Nr):
-        rxn   = eval("lambda energy :" + reactionExpressionslist[i])
-        rxn_T = eval("lambda energy :" + reactionTExpressionslist[i])
-
-        reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
-                            kf = rxn, kf_T = rxn_T)
-        reactionsList.append(reaction)
-
-    params.reactionsList = reactionsList
-    #params.Nr = 1
-
-    # 5) Dump to screen
-    params.print()
diff --git a/psaapPropertiesCurrentTestCase100mTorr.py b/psaapPropertiesCurrentTestCase100mTorr.py
deleted file mode 100644
index 60bf77c14..000000000
--- a/psaapPropertiesCurrentTestCase100mTorr.py
+++ /dev/null
@@ -1,235 +0,0 @@
-import numpy as np
-
-
-class Reaction(object):
-    def __init__(self, *initial_data, **kwargs):
-        for dictionary in initial_data:
-            for key in dictionary:
-                setattr(self, key, dictionary[key])
-        for key in kwargs:
-            setattr(self, key, kwargs[key])
-
-def setPsaapPropertiesCurrentTestCase100mTorr(gam, inputV0, inputVDC, params, Nr, iSample):
-    """Sets non-dimensional properties corresponding to Liu 2014 paper.
-
-    Inputs:
-      gam : Secondary electron emission coefficient
-      params : chebSolver.modelParams class
-
-    Outputs: None
-      params data is overwritten using values from Liu 2014.
-    """
-    ###################################################################
-    # User specified parameters (you may change these if you wish to
-    # run a different scenario from Liu 2014)
-    ###################################################################
-
-    # densities
-    nAr = 3.22e21   # background number density of Ar [1/m^3] (corresponds to p=100 mTorr)
-    np0 = 8e16        # "nominal" electron density [1/m^3]
-
-    # masses
-    # me = 9.10938356e-31        # mass of an electron [kg]
-    # me = 5.489e-4              # mass of an electron [u]
-    me = 0.511e6                 # mass of an electron [eV/c2]
-    # mAr = 39.948               # mass of an argon atom [u]
-    # mAr = 39.948 * 1.66054e-27 # mass of an argon atom [kg]
-    mAr = 37.2158e9              # mass of an electron [eV/c2]
-    # u = 931.4941e6             # eV/c2
-    c = 299792458                # speed of light [m/s]
-    se = 40                      # momentum cross section [m^2]
-
-    # nominal electron energy
-    e0 = 1.0  # [eV]
-
-    # pressure
-    # p  = 133.3224*1.5      # [J/m^3] *1.5 to convert it to energy (1 Torr)
-    p  = 2 * 6.6661185 * 1.5 # [J/m^3] *1.5 to convert it to energy (50 mTorr)
-
-    # gas energy at the wall
-    Tg0 = 0.038778    # 3/2*300K*kB ~ (p0 - nT[:,0])/ntot
-
-    # characteristics of driving voltage
-    V0  = inputV0                 # amplitude of driving voltage [V]
-    verticalShift = inputVDC      # DC voltage (vertical shift in driving voltage)
-    # tau = (1./13.6e6)           # period of driving voltage [s]
-    tau = (1./27.1e6)             # period of driving voltage [s]
-    L   = 2.00*0.005              #1*0.005  # half-gap-width [m] (gap width is 1cm)
-    electrodeArea = np.pi*0.05**2 # electrode area [m^2] (electrode diameter = 0.1 m)
-
-    # transport parameters
-    nmue = 9.66e20   # argon number density times electron mobility [1/(V*cm*s)]
-    nmui = 4.65e18   # argon number density times ion mobility [1/(V*cm*s)]
-    nmum = 1 / (np.sqrt(16.0 * (mAr + mAr) * 300 * 8.62e-5 * c**2
-                        / (3.0 * np.pi * mAr * mAr)) * se * mAr * 1.6e-19 / c**2)
-    nDe  = 3.86e21   # argon number density times electron diffusivity [1/(cm*s)]
-    nDi  = 2.07e17   # argon number density times ion diffusivity [1/(cm*s)]
-    nDm  = 2.42e17   # argon number density times metastable diffusivity [1/(cm*s)]
-
-    # reaction parameters (NB: k_i = Ck*exp(-A/Te))
-    #Ck = np.array([1.235e-7,3.712e-8,2.05e-7,1.818e-9,2e-7]) # pre-exponential factors [cm^3/s]
-    #A  = np.array([18.687,15.06,4.95,2.14,0.0])      # activation temperature [eV]
-    #dH = np.array([15.7,11.56,4.14,-11.56,0.0])        # energy lost per electron due to ionization rxn [eV]
-
-    #Ck = np.array([1.235e-7,3.712e-8,2.05e-7,1.818e-9]) # pre-exponential factors [cm^3/s]
-    #A  = np.array([18.687,15.06,4.95,2.14])      # activation temperature [eV]
-    #dH = np.array([15.7,11.56,4.14,-11.56])        # energy lost per electron due to ionization rxn [eV]
-
-    #Ck = np.array([1.235e-7,0.0,0.0,0.0,0.0]) # pre-exponential factors [cm^3/s]
-    #A  = np.array([18.687,15.06,4.95,2.14,0.0])      # activation temperature [eV]
-    #dH = np.array([15.7,0.0,0.0,0.0,0.0])        # energy lost per electron due to ionization rxn [eV]
-
-    # nominal
-    Ck = np.array([1.235e-7,3.712e-8,2.05e-7,1.818e-9,2e-7,6.2e-10,3.0e-15,1.1e-31]) # pre-exponential factors [cm^3/s]
-
-    # slow excitation rate
-    #Ck = np.array([1.235e-7,0.5*3.712e-8,2.05e-7,1.818e-9,2e-7,6.2e-10,3.0e-15,1.1e-31]) # pre-exponential factors [cm^3/s]
-
-    ## fast excitation rate
-    #Ck = np.array([1.235e-7,2.0*3.712e-8,2.05e-7,1.818e-9,2e-7,6.2e-10,3.0e-15,1.1e-31]) # pre-exponential factors [cm^3/s]
-
-    A  = np.array([18.687,15.06,4.95,2.14,0.0,0.0,0.0,0.0]) # activation temperature [eV]
-    dH = np.array([15.7,11.56,4.14,-11.56,0.0,0.0,0.0,0.0]) # energy lost per electron due to ionization rxn [eV]
-    dEps = np.array([0.0,15.7,11.56,0.0])
-
-    # BC parameters
-    # ks = 1.19e7  # electron recombination rate [cm/s]
-    ks = 1.366109824889323e7 # electron recombination rate [cm/s/eV]
-
-    ###################################################################
-    # Constants of nature (probably shouldn't change unless you have
-    # root privileges on universe)
-    ###################################################################
-    qe   = 1.6e-19   # unit charge [C]
-    eps0 = 8.86e-12  # permittivity of free space [F/m]
-    kB   = 1.38e-23  # Boltzmann constant [J/K]
-    # kB   = 8.62e−5 # Boltzmann constant [eV/K]
-
-
-    ###################################################################
-    # Calculate non-dimensional parameters
-    ###################################################################
-
-    # 1) Convert input units to base SI (except eV)
-    nDe  *= 100. # 1/(m*s)
-    nDi  *= 100. # 1/(m*s)
-    nDm  *= 100.
-    nmue *= 100. # 1/(V*m*s)
-    nmui *= 100. # 1/(V*m*s)
-    Ck   *= 1e-6 # m^3/s
-    Ck[7] *= 1e-6 # Ck[7] is now in m^6/s
-    ks   *= 0.01 # m/s
-    se   *= 1.0e-20  # m^2
-
-    # 2) Compute "raw" transport parameters
-    De  = nDe/nAr
-    Di  = nDi/nAr
-    Dm  = nDm/nAr
-
-    mue = nmue/nAr
-    mui = nmui/nAr
-    mum = nmum/nAr
-
-    # 3) Compute non-dimensional properties required by solver
-    De    = De*tau/(L*L)
-    Di    = Di*tau/(L*L)
-    Dm    = Dm*tau/(L*L)
-
-    mue   = mue*V0*tau/(L*L)
-    mui   = mui*V0*tau/(L*L)
-    mum   = mum*V0*tau/(L*L)
-
-    Ck[0:2] = Ck[0:2]*tau*nAr
-    #Ck[2:5] = Ck[2:5]*tau*np0
-    Ck[2:6] = Ck[2:6]*tau*np0
-    Ck[6]  *= tau*nAr
-    Ck[7]  *= tau*nAr*nAr
-    A       = A*1.5/e0  # 1.5 to convert from temperature to energy
-    dH      = dH/e0
-    qStar   = V0/e0 # qe*V0/e0, since e0 in eV, need qe*V0 in eV, which is just V0 in V
-    alpha   = qe*np0*L*L/(V0*eps0)
-    ks      = ks*tau/L
-    p0      = p/qe/np0
-    kappaB  = 4.878171165833662*0.144 # non-dimensional thermal conductivity of background specie
-                                      # (2/3)*tau/L**2*Kb/np0/kB,
-                                      # where Kb is the thermal conductivity of background specie
-
-    #params.beta = np.array([[2,2,2,1,1],[1,0,1,0,0],[0,1,0,0,0],[0,0,0,1,1]])
-    #params.alfa = np.array([[1,1,1,1,1],[0,0,0,0,0],[0,0,1,1,1],[1,1,0,0,0]])
-    #params.beta = np.array([[2,1,2,1],[1,0,1,0],[0,1,0,0],[0,0,0,1]], dtype=np.int)
-    #params.alfa = np.array([[1,1,1,1],[0,0,0,0],[0,0,1,1],[1,1,0,0]], dtype=np.int)
-    params.beta = np.array([[2,1,2,1,1,1,0,0],[1,0,1,0,0,1,0,0],[0,1,0,0,0,0,0,0],[0,0,0,1,0,1,2,1]], dtype=np.int64)
-    params.alfa = np.array([[1,1,1,1,1,0,0,0],[0,0,0,0,0,0,0,0],[0,0,1,1,1,2,1,1],[1,1,0,0,0,0,1,2]], dtype=np.int64)
-
-    # 4) Set values in params class
-    params.D[0]    = De
-    params.D[1]    = Di
-    params.D[2]    = Dm
-
-    params.mu[0]   = mue
-    params.mu[1]   = mui
-    params.mu[2]   = mum
-
-    params.A[:]    = Ck[:]
-    params.B[:]    = 0.0
-    params.C[:]    = A[:]
-
-    params.dH[:]   = dH[:]
-    params.dEps[:] = dEps[:]
-    params.qStar   = qStar
-    params.alpha   = alpha
-    params.ks      = ks
-    params.gam     = gam
-    params.kappaB  = kappaB
-    params.nAronp0 = nAr / np0
-    params.p0      = p0
-    params.Tg0     = Tg0
-    params.EC      = 2.0 * me / mAr \
-        * np.sqrt(16.0 * (me + mAr) * e0 * c**2
-                  / (3.0 * np.pi * me * mAr)) * se * nAr * tau
-    # params.EC = 2.0 * me / mAr * 3.8e9 * tau
-
-    params.verticalShift = verticalShift / V0
-
-    # Parameters needed to compute the current with dimensions
-    params.V0Ltau  = V0 / (L * tau)
-    params.V0L     = V0 / L
-    params.LLV0tau = (L*L) / (V0*tau)
-    params.tauL    = L / tau
-    params.np0     = np0           # "nominal" electron density [1/m^3]
-    params.qe      = qe            # unit charge [C]
-    params.eps0    = eps0          # unit charge [C]
-    params.eArea   = electrodeArea # electrode area [m^2]
-
-    reactionExpressionslist = [f"{params.A[0]} * energy**{params.B[0]} * np.exp(-{params.C[0]} / energy)",
-                               f"{params.A[1]} * energy**{params.B[1]} * np.exp(-{params.C[1]} / energy)",
-                               f"{params.A[2]} * energy**{params.B[2]} * np.exp(-{params.C[2]} / energy)",
-                               f"{params.A[3]} * energy**{params.B[3]} * np.exp(-{params.C[3]} / energy)",
-                               f"{params.A[4]} * energy**{params.B[4]} * np.exp(-{params.C[4]} / energy)",
-                               f"{params.A[5]} * energy**{params.B[5]} * np.exp(-{params.C[5]} / energy)",
-                               f"{params.A[6]} * energy**{params.B[6]} * np.exp(-{params.C[6]} / energy)",
-                               f"{params.A[7]} * energy**{params.B[7]} * np.exp(-{params.C[7]} / energy)"]
-
-    reactionTExpressionslist = [f"{params.A[0]} * (energy**({params.B[0]}-1)) * np.exp(-{params.C[0]}/energy) * ({params.B[0]} + {params.C[0]}/energy)",
-                                f"{params.A[1]} * (energy**({params.B[1]}-1)) * np.exp(-{params.C[1]}/energy) * ({params.B[1]} + {params.C[1]}/energy)",
-                                f"{params.A[2]} * (energy**({params.B[2]}-1)) * np.exp(-{params.C[2]}/energy) * ({params.B[2]} + {params.C[2]}/energy)",
-                                f"{params.A[3]} * (energy**({params.B[3]}-1)) * np.exp(-{params.C[3]}/energy) * ({params.B[3]} + {params.C[3]}/energy)",
-                                f"{params.A[4]} * (energy**({params.B[4]}-1)) * np.exp(-{params.C[4]}/energy) * ({params.B[4]} + {params.C[4]}/energy)",
-                                f"{params.A[5]} * (energy**({params.B[5]}-1)) * np.exp(-{params.C[5]}/energy) * ({params.B[5]} + {params.C[5]}/energy)",
-                                f"{params.A[6]} * (energy**({params.B[6]}-1)) * np.exp(-{params.C[6]}/energy) * ({params.B[6]} + {params.C[6]}/energy)",
-                                f"{params.A[7]} * (energy**({params.B[7]}-1)) * np.exp(-{params.C[7]}/energy) * ({params.B[7]} + {params.C[7]}/energy)"]
-
-    reactionsList = []
-    for i in range(Nr):
-        rxn   = eval("lambda energy :" + reactionExpressionslist[i])
-        rxn_T = eval("lambda energy :" + reactionTExpressionslist[i])
-
-        reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
-                            kf = rxn, kf_T = rxn_T)
-        reactionsList.append(reaction)
-
-    params.reactionsList = reactionsList
-    #params.Nr = 1
-
-    # 5) Dump to screen
-    params.print()
diff --git a/psaapPropertiesTestArmInterpTrans.py b/psaapPropertiesTestArmInterpTrans.py
index c1c6f0626..22684fc3a 100644
--- a/psaapPropertiesTestArmInterpTrans.py
+++ b/psaapPropertiesTestArmInterpTrans.py
@@ -308,15 +308,6 @@ def setPsaapPropertiesTestArmInterpTrans(gam, inputV0, inputVDC, params, Nr, iSa
     diffList.append(diffusivity)
 
     Tplt = np.linspace(-0.5, 5, 1025)
-    import matplotlib.pyplot as plt
-
-    plt.figure()
-    plt.plot(Tplt, De_spline(Tplt), 'b-')
-    plt.plot(Tplt, De * np.ones(Tplt.shape), 'k--')
-    plt.plot(Te, De_interp, 'rx')
-    #plt.plot(Tplt, De_Te_spline(Tplt), 'g--')
-    plt.xlim(-1, 5)
-    plt.show()
 
     Ns = 4
     for i in range(1, Ns):
diff --git a/psaapPropertiesTestJP.py b/psaapPropertiesTestJP.py
deleted file mode 100644
index c826f9b0d..000000000
--- a/psaapPropertiesTestJP.py
+++ /dev/null
@@ -1,396 +0,0 @@
-import numpy as np
-from scipy.interpolate import CubicSpline
-
-import matplotlib.pyplot as plt
-import matplotlib.colors as mcolors
-
-import logging
-
-class Reaction(object):
-    def __init__(self, *initial_data, **kwargs):
-        for dictionary in initial_data:
-            for key in dictionary:
-                setattr(self, key, dictionary[key])
-        for key in kwargs:
-            setattr(self, key, kwargs[key])
-
-def setPsaapPropertiesTestJP(gam, inputV0, inputVDC, params, Nr, iSample):
-    """Sets non-dimensional properties corresponding to Liu 2014 paper.
-
-    Inputs:
-      gam : Secondary electron emission coefficient
-      params : chebSolver.modelParams class
-
-    Outputs: None
-      params data is overwritten using values from Liu 2014.
-    """
-    ###################################################################
-    # User specified parameters (you may change these if you wish to
-    # run a different scenario from Liu 2014)
-    ###################################################################
-
-    # densities
-    nAr = 3.22e22     # background number density of Ar [1/m^3] (corresponds to p=100 mTorr)
-    np0 = 8e16        # "nominal" electron density [1/m^3]
-
-    # masses
-    # me = 9.10938356e-31        # mass of an electron [kg]
-    # me = 5.489e-4              # mass of an electron [u]
-    me = 0.511e6                 # mass of an electron [eV/c2]
-    # mAr = 39.948               # mass of an argon atom [u]
-    # mAr = 39.948 * 1.66054e-27 # mass of an argon atom [kg]
-    mAr = 37.2158e9              # mass of an electron [eV/c2]
-    # u = 931.4941e6             # eV/c2
-    c = 299792458                # speed of light [m/s]
-    se = 40                      # momentum cross section [A^2]
-
-    # nominal electron energy
-    e0 = 1.0  # [eV]
-
-    # pressure
-    p  = 133.3224*1.5      # [J/m^3] *1.5 to convert it to energy (1 Torr)
-
-    # gas energy at the wall
-    Tg0 = 0.038778    # 3/2*300K*kB ~ (p0 - nT[:,0])/ntot
-
-    # characteristics of driving voltage
-    V0  = inputV0                 # amplitude of driving voltage [V]
-    verticalShift = inputVDC      # DC voltage (vertical shift in driving voltage)
-    tau = (1./13.6e6)             # period of driving voltage [s]
-    L   = 2.00*0.005              # half-gap-width [m] (gap width is 2.54cm)
-    electrodeArea = np.pi*0.05**2 # electrode area [m^2] (electrode diameter = 0.1 m)
-
-    # transport parameters
-    nmue = 9.66e21   # argon number density times electron mobility [1/(V*cm*s)]
-    nmui = 4.65e19   # argon number density times ion mobility [1/(V*cm*s)]
-    #nmum = 1 / (np.sqrt(16.0 * (mAr + mAr) * 300 * 8.62e-5 * c**2
-    #                    / (3.0 * np.pi * mAr * mAr)) * se * mAr * 1.6e-19 / c**2)
-    nmum = 0.0
-    nDe  = 3.86e22   # argon number density times electron diffusivity [1/(cm*s)]
-    nDi  = 2.07e18   # argon number density times ion diffusivity [1/(cm*s)]
-    nDm  = 2.42e18   # argon number density times metastable diffusivity [1/(cm*s)]
-
-    # reaction parameters (NB: k_i = Ck*Ee^B*exp(-A/Ee))
-    #                          Ee = 3/2*Te (Te in eV)
-    #                          -> k_i = [Ck*(2/3)^B] * Ee^B * exp[-(3/2)*A/Ee]
-    # nominal
-    Ck = np.array([1.235e-7,3.712e-8,2.05e-7,4.0e-13,5.0e-10,4.3e-10,2.1e-15,10.0,5.0e-27]) # pre-exponential factors [cm^3/s]
-    B  = np.array([0.0,0.0,0.0,-0.5,0,0.74,0,0,-4.5])
-    A  = np.array([18.687,15.06,4.95,0.0,0.0,0.0,0.0,0.0,0.0]) # activation temperature [eV]
-    dH = np.array([15.76,11.56,4.2,0.0,-7.36,-11.56,-11.56,0.0,-4.2]) # energy lost per electron due to ionization rxn [eV]
-    dEps = np.array([0.0,15.76,11.56,0.0])
-
-    # BC parameters
-    # ks = 1.19e7  # electron recombination rate [cm/s]
-    ks = 1.366109824889323e7 # electron recombination rate [cm/s/eV]
-
-    ###################################################################
-    # Constants of nature (probably shouldn't change unless you have
-    # root privileges on universe)
-    ###################################################################
-    qe   = 1.6e-19   # unit charge [C]
-    eps0 = 8.86e-12  # permittivity of free space [F/m]
-    kB   = 1.38e-23  # Boltzmann constant [J/K]
-    # kB   = 8.62e−5 # Boltzmann constant [eV/K]
-
-
-    ###################################################################
-    # Calculate non-dimensional parameters
-    ###################################################################
-
-    # 1) Convert input units to base SI (except eV)
-    nDe  *= 100. # 1/(m*s)
-    nDi  *= 100. # 1/(m*s)
-    nDm  *= 100.
-    nmue *= 100. # 1/(V*m*s)
-    nmui *= 100. # 1/(V*m*s)
-    Ck[0:7] *= 1e-6 # m^3/s
-    Ck[7] *= 1 # 1/s
-    Ck[8] *= 1e-12 # m^6/s
-    ks   *= 0.01 # m/s
-    se   *= 1.0e-20  # m^2
-
-    # 2) Compute "raw" transport parameters
-    De  = nDe/nAr
-    Di  = nDi/nAr
-    Dm  = nDm/nAr
-
-    mue = nmue/nAr
-    mui = nmui/nAr
-    mum = nmum/nAr
-
-    # 3) Compute non-dimensional properties required by solver
-    De    = De*tau/(L*L)
-    Di    = Di*tau/(L*L)
-    Dm    = Dm*tau/(L*L)
-
-    mue   = mue*V0*tau/(L*L)
-    mui   = mui*V0*tau/(L*L)
-    mum   = mum*V0*tau/(L*L)
-
-    Ck[0:2] = Ck[0:2]*tau*nAr
-    #Ck[2:5] = Ck[2:5]*tau*np0
-    Ck[2:6] = Ck[2:6]*tau*np0
-    Ck[6] *= tau*nAr
-    Ck[7] *= tau
-    Ck[8] *= tau*np0*np0
-    # Ck[7]  *= tau*nAr*nAr
-    A       = A*1.5/e0  # 1.5 to convert from temperature to energy
-    dH      = dH/e0
-    qStar   = V0/e0 # qe*V0/e0, since e0 in eV, need qe*V0 in eV, which is just V0 in V
-    alpha   = qe*np0*L*L/(V0*eps0)
-    ks      = ks*tau/L
-    p0      = p/qe/np0
-    kappaB  = 4.878171165833662*1.6129 # non-dimensional thermal conductivity of background specie
-                                # (2/3)*tau/L**2*Kb/np0/kB,
-                                # where Kb is the thermal conductivity of background specie
-
-    #params.beta = np.array([[2,2,2,1,1],[1,0,1,0,0],[0,1,0,0,0],[0,0,0,1,1]])
-    #params.alfa = np.array([[1,1,1,1,1],[0,0,0,0,0],[0,0,1,1,1],[1,1,0,0,0]])
-    #params.beta = np.array([[2,1,2,1],[1,0,1,0],[0,1,0,0],[0,0,0,1]], dtype=np.int)
-    #params.alfa = np.array([[1,1,1,1],[0,0,0,0],[0,0,1,1],[1,1,0,0]], dtype=np.int)
-    # params.beta = np.array([[2,1,2,1,1,1,0,0],[1,0,1,0,0,1,0,0],[0,1,0,0,0,0,0,0],[0,0,0,1,0,1,2,1]], dtype=np.int64)
-    # params.alfa = np.array([[1,1,1,1,1,0,0,0],[0,0,0,0,0,0,0,0],[0,0,1,1,1,2,1,1],[1,1,0,0,0,0,1,2]], dtype=np.int64)
-    params.beta = np.array([[2,1,2,0,1,1,0,0,1],[1,0,1,0,1,0,0,0,0],[0,1,0,1,0,0,0,0,1],[0,0,0,0,1,1,2,1,0]], dtype=np.int64)
-    params.alfa = np.array([[1,1,1,1,0,1,0,0,2],[0,0,0,1,0,0,0,0,1],[0,0,1,0,2,1,1,1,0],[1,1,0,0,0,0,1,0,0]], dtype=np.int64)
-	# Rxn1:  E + AR   ->  2E  +  AR+
-	# Rxn2:  E + AR   ->  E   +  AR*
-	# Rxn3:  E + AR*  ->  2E  +  AR+
-	# Rxn4:  E + AR+  ->  AR*
-	# Rxn5:  2AR*     ->  E   +  AR+  +  AR
-	# Rxn6:  E + AR*  ->  E   +  AR
-	# Rxn7:  AR* + AR ->  2AR
-	# Rxn8:  AR*      ->  AR
-	# Rxn9:  2E + AR+ ->  E   +  AR*
-
-    # 4) Set values in params class
-    params.D[0]    = De
-    params.D[1]    = Di
-    params.D[2]    = Dm
-
-    params.mu[0]   = mue
-    params.mu[1]   = mui
-    params.mu[2]   = mum
-
-    params.A[:]    = Ck[:]
-    params.B[:]    = B[:]
-    params.C[:]    = A[:]
-
-    # Account for the 2/3 term to convert from electron temperature to electron energy
-    for i in range(len(params.A)):
-        params.A[i] *= (2/3)**(params.B[i])
-
-    params.dH[:]   = dH[:]
-    params.dEps[:] = dEps[:]
-    params.qStar   = qStar
-    params.alpha   = alpha
-    params.ks      = ks
-    params.gam     = gam
-    params.kappaB  = kappaB
-    params.nAronp0 = nAr / np0
-    params.p0      = p0
-    params.Tg0     = Tg0
-    params.EC      = 2.0 * me / mAr \
-        * np.sqrt(16.0 * (me + mAr) * e0 * c**2
-                  / (3.0 * np.pi * me * mAr)) * se * nAr * tau
-    # params.EC = 2.0 * me / mAr * 3.8e9 * tau
-
-    params.verticalShift = verticalShift / V0
-
-    # Parameters needed to compute the current with dimensions
-    params.V0Ltau  = V0 / (L * tau)
-    params.V0L     = V0 / L
-    params.LLV0tau = (L*L) / (V0*tau)
-    params.tauL    = L / tau
-    params.np0     = np0           # "nominal" electron density [1/m^3]
-    params.qe      = qe            # unit charge [C]
-    params.eps0    = eps0          # unit charge [C]
-    params.eArea   = electrodeArea # electrode area [m^2]
-
-    # reactionExpressionslist = [f"{params.A[0]} * energy**{params.B[0]} * np.exp(-{params.C[0]} / energy)",
-    #                            f"{params.A[1]} * energy**{params.B[1]} * np.exp(-{params.C[1]} / energy)",
-    #                            f"{params.A[2]} * energy**{params.B[2]} * np.exp(-{params.C[2]} / energy)",
-    #                            f"{params.A[3]} * energy**{params.B[3]} * np.exp(-{params.C[3]} / energy)",
-    #                            f"{params.A[4]} * energy**{params.B[4]} * np.exp(-{params.C[4]} / energy)",
-    #                            f"{params.A[5]} * energy**{params.B[5]} * np.exp(-{params.C[5]} / energy)",
-    #                            f"{params.A[6]} * energy**{params.B[6]} * np.exp(-{params.C[6]} / energy)",
-    #                            f"{params.A[7]} * energy**{params.B[7]} * np.exp(-{params.C[7]} / energy)"]
-
-    # reactionTExpressionslist = [f"{params.A[0]} * (energy**({params.B[0]}-1)) * np.exp(-{params.C[0]}/energy) * ({params.B[0]} + {params.C[0]}/energy)",
-    #                             f"{params.A[1]} * (energy**({params.B[1]}-1)) * np.exp(-{params.C[1]}/energy) * ({params.B[1]} + {params.C[1]}/energy)",
-    #                             f"{params.A[2]} * (energy**({params.B[2]}-1)) * np.exp(-{params.C[2]}/energy) * ({params.B[2]} + {params.C[2]}/energy)",
-    #                             f"{params.A[3]} * (energy**({params.B[3]}-1)) * np.exp(-{params.C[3]}/energy) * ({params.B[3]} + {params.C[3]}/energy)",
-    #                             f"{params.A[4]} * (energy**({params.B[4]}-1)) * np.exp(-{params.C[4]}/energy) * ({params.B[4]} + {params.C[4]}/energy)",
-    #                             f"{params.A[5]} * (energy**({params.B[5]}-1)) * np.exp(-{params.C[5]}/energy) * ({params.B[5]} + {params.C[5]}/energy)",
-    #                             f"{params.A[6]} * (energy**({params.B[6]}-1)) * np.exp(-{params.C[6]}/energy) * ({params.B[6]} + {params.C[6]}/energy)",
-    #                             f"{params.A[7]} * (energy**({params.B[7]}-1)) * np.exp(-{params.C[7]}/energy) * ({params.B[7]} + {params.C[7]}/energy)"]
-
-    reactionExpressionslist = [f"{params.A[0]} * energy**{params.B[0]} * np.exp(-{params.C[0]} / energy)",
-                                f"{params.A[1]} * energy**{params.B[1]} * np.exp(-{params.C[1]} / energy)",
-                                f"{params.A[2]} * energy**{params.B[2]} * np.exp(-{params.C[2]} / energy)",
-                                f"{params.A[3]} * energy**{params.B[3]} * np.exp(-{params.C[3]} / energy)",
-                                f"{params.A[4]} * energy**{params.B[4]} * np.exp(-{params.C[4]} / energy)",
-                                f"{params.A[5]} * energy**{params.B[5]} * np.exp(-{params.C[5]} / energy)",
-                                f"{params.A[6]} * energy**{params.B[6]} * np.exp(-{params.C[6]} / energy)",
-				f"{params.A[7]} * energy**{params.B[7]} * np.exp(-{params.C[7]} / energy)",
-				f"{params.A[8]} * energy**{params.B[8]} * np.exp(-{params.C[8]} / energy)"]
-
-    reactionTExpressionslist = [f"{params.A[0]} * (energy**({params.B[0]}-1)) * np.exp(-{params.C[0]}/energy) * ({params.B[0]} + {params.C[0]}/energy)",
-                                f"{params.A[1]} * (energy**({params.B[1]}-1)) * np.exp(-{params.C[1]}/energy) * ({params.B[1]} + {params.C[1]}/energy)",
-                                f"{params.A[2]} * (energy**({params.B[2]}-1)) * np.exp(-{params.C[2]}/energy) * ({params.B[2]} + {params.C[2]}/energy)",
-                                f"{params.A[3]} * (energy**({params.B[3]}-1)) * np.exp(-{params.C[3]}/energy) * ({params.B[3]} + {params.C[3]}/energy)",
-                                f"{params.A[4]} * (energy**({params.B[4]}-1)) * np.exp(-{params.C[4]}/energy) * ({params.B[4]} + {params.C[4]}/energy)",
-                                f"{params.A[5]} * (energy**({params.B[5]}-1)) * np.exp(-{params.C[5]}/energy) * ({params.B[5]} + {params.C[5]}/energy)",
-                                f"{params.A[6]} * (energy**({params.B[6]}-1)) * np.exp(-{params.C[6]}/energy) * ({params.B[6]} + {params.C[6]}/energy)",
-				f"{params.A[7]} * (energy**({params.B[7]}-1)) * np.exp(-{params.C[7]}/energy) * ({params.B[7]} + {params.C[7]}/energy)",
-				f"{params.A[8]} * (energy**({params.B[8]}-1)) * np.exp(-{params.C[8]}/energy) * ({params.B[8]} + {params.C[8]}/energy)"]
-
-    reactionExpressionTypelist =  np.array([True,True,True,False,False,False,False,False,False])
-
-    reactionsList = []
-    LOGFilename = 'interpolationSample%s.log'%str(iSample)
-    f = open(LOGFilename, 'w')
-	# import h5py as h5
-	# rxnName = ["Ionization", ...] <- dictionary containing reaction name root string
-	# for r in range(Nr):
-	#	if reactionExpressionTypeList[r]:
-	#		fileName = "{0:s}.{1:08d}.h5".format(rxnName[r], iSample)
-	#		f = h5.File(filename, "r")
-	#		D = f["table"]
-
-    for i in range(Nr):
-        if reactionExpressionTypelist[i]:
-            Nsample = 72
-            N300 = 200
-
-            root_dir = "/g/g92/jbarbere/glowDischarge/toyProblems/timeDomain"
-            rate_file = "{0:s}/BOLSIGChemistry/reaction300K_{1:d}.dat".format(root_dir, i)
-            temp_file = "{0:s}/BOLSIGChemistry/reaction300K.Te.dat".format(root_dir)
-
-            #rateCoeff = np.fromfile('/usr/workspace/violetak/BOLSIGSamples/glowDischarge/toyProblems/timeDomain/BOLSIGChemistry/reaction300K_%s.dat' %str(i))
-            rateCoeff = np.fromfile(rate_file)
-            rateCoeff = np.reshape(rateCoeff,[Nsample, N300]).T[:,iSample]
-
-            #Te = np.fromfile('/usr/workspace/violetak/BOLSIGSamples/glowDischarge/toyProblems/timeDomain/BOLSIGChemistry/reaction300K.Te.dat')
-            Te = np.fromfile(temp_file)
-            Te = np.reshape(Te,[Nsample, N300]).T[:,iSample]
-
-            # Sorting mean energy array and rate coefficient array based on
-            # the mean energy array.
-            Teinds = Te.argsort()
-            rateCoeff = rateCoeff[Teinds]
-            Te = Te[Teinds]
-
-            # Find duplicates
-            TeDuplicateinds = np.where(np.abs(np.diff(Te, axis=0)) > 0.0)
-            TeDuplicateindsForLog = np.where(np.abs(np.diff(Te, axis=0)) == 0.0)
-            rateCoeff = rateCoeff[TeDuplicateinds]
-            Te = Te[TeDuplicateinds]
-
-            # Nondimensionalization of mean energy.
-            # Te *= 1.5
-
-            # Find first non-zero value of the coefficient rate.
-            I = np.nonzero(rateCoeff)
-
-            diffRateCoeff = [j-i for i, j in zip(rateCoeff[:-1], rateCoeff[1:])]
-            diffTe = [j-i for i, j in zip(Te[:-1], Te[1:])]
-
-            Monotonicity = np.asarray([j/i for i, j in zip(diffTe, diffRateCoeff)])
-            Monotonicity = np.insert(Monotonicity, 0, 0.0, axis=0)
-
-            Nan = np.isnan(Monotonicity)
-            Inf = np.isinf(Monotonicity)
-            indexPositive = np.where(Monotonicity>0.0)
-            Positive = np.full(Monotonicity.shape, False, dtype=bool)
-            Positive[indexPositive] = True
-
-            indices = Nan + Inf + Positive
-
-            lastFalse = np.where(indices==False)[-1][-1] + 2
-
-            # Transformation to log scale.
-            TeLog = np.log(Te)
-
-            # Compute the slope of the rate coefficient between its first two non-zero values.
-            # Finite differences are used.
-            dydx = (rateCoeff[lastFalse + 1] - rateCoeff[lastFalse]) \
-                 / (Te[lastFalse + 1] - Te[lastFalse])
-
-            # Arrhenius form: kf = A * exp(-C / Te)
-            C = Te[lastFalse]**2.0*dydx / rateCoeff[lastFalse]
-
-            # Compute pre-exponential coefficient, A, in log scale.
-            ALog = np.log(rateCoeff[lastFalse]) + C / Te[lastFalse]
-
-            # Transform rate coefficient in log scale.
-            rateCoeffLog = np.zeros(rateCoeff.shape)
-            rateCoeffLog[lastFalse:] = np.log(rateCoeff[lastFalse:])
-            # For the troublesome values, we use the Arrhenius form.
-            rateCoeffLog[0:lastFalse] = ALog - C / Te[0:lastFalse]
-            # Nondimensionalization in log scale.
-            if i < 2:
-                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
-            else:
-                rateCoeffLog += - np.log(1.0/tau) + np.log(np0)
-
-            # Interpolation in log scale.
-            reactionExpressionsLog = CubicSpline(TeLog, rateCoeffLog)
-            # Gradient in log scale
-            reactionTExpressionsLog = CubicSpline.derivative(reactionExpressionsLog)
-
-            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
-                                rxnBolsig = reactionExpressionTypelist[i],
-                                kf_log = reactionExpressionsLog,
-                                kf_T_log = reactionTExpressionsLog)
-            reactionsList.append(reaction)
-
-            logging.basicConfig(filename=LOGFilename)
-            logging.warning('Interpolation info (Reaction %s):', i)
-            logging.warning('First non-zero entry in the rate coefficient: %s', I[0][0])
-            logging.warning('Monotonicity of the rate coefficient start from entry: %s', lastFalse)
-            logging.warning('Position of possible duplicates in mean energy array: %s',
-                          TeDuplicateindsForLog[0])
-
-            # rxn   = eval("lambda energy :" + reactionExpressionslist[i])
-            # rxn_T = eval("lambda energy :" + reactionTExpressionslist[i])
-            # # setting the axes at the centre
-            # fig ,ax = plt.subplots(figsize=(9, 6))
-            # ax.spines["top"].set_visible(True)
-            # ax.spines["right"].set_visible(True)
-            # ax.set_yscale('log')
-            # ax.set_xscale('log')
-
-            # # plot the function
-            # # plt.plot(rateCoeffXFiner, np.exp(reactionExpressions_cubicSplineDerivative_log(rateCoeffXFiner)),
-            # #  		 color='salmon', linestyle='--', label='interBolsig')
-            # # plt.plot(rateCoeffXFine, np.exp(reactionExpressions_cubicSpline_log(rateCoeffXFine)),
-            # #  		 color='lightgreen', linestyle='--', label='interBolsig')
-            # # plt.plot(Te[:,0], reactionTExpressionsLogFiltered(TeLog[:]) * np.exp(reactionExpressionsLog(TeLog[:])) / Te[:,0],
-            # #  		 color='blue', linestyle='-', label='interBolsig')
-            # plt.plot(Te, np.exp(reactionExpressionsLog(TeLog[:])),
-            #  		 color='green', linestyle='-', label='Bolsig')
-            # plt.plot(Te, rxn(Te),
-            #  		 color='salmon', linestyle='--', label='Liu')
-            # # plt.plot(Te[:,0], rxn_T(Te[:,0]),
-            # #  		 color='red', linestyle='--', label='interBolsig')
-            # plt.xlim((0.05,100))
-            # plt.ylim((1e-50,300))
-            # plt.legend()
-            # plt.savefig("./Rates_%s.pdf" %str(i), dpi=300)
-            # # plt.xlim((-0.0001,0.0255))
-            # plt.show()
-        else:
-            rxn   = eval("lambda energy :" + reactionExpressionslist[i])
-            rxn_T = eval("lambda energy :" + reactionTExpressionslist[i])
-
-            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
-                                rxnBolsig = reactionExpressionTypelist[i],
-                                kf = rxn, kf_T = rxn_T)
-            reactionsList.append(reaction)
-
-    params.reactionsList = reactionsList
-    #params.Nr = 1
-
-    # 5) Dump to screen
-    params.print()
diff --git a/psaapPropertiesTestJP_Arrhenius.py b/psaapPropertiesTestJP_Arrhenius.py
deleted file mode 100755
index 84b12f6e5..000000000
--- a/psaapPropertiesTestJP_Arrhenius.py
+++ /dev/null
@@ -1,373 +0,0 @@
-import numpy as np
-from scipy.interpolate import CubicSpline
-import csv
-import matplotlib.pyplot as plt
-import matplotlib.colors as mcolors
-
-import logging
-
-class Reaction(object):
-    def __init__(self, *initial_data, **kwargs):
-        for dictionary in initial_data:
-            for key in dictionary:
-                setattr(self, key, dictionary[key])
-        for key in kwargs:
-            setattr(self, key, kwargs[key])
-
-def setPsaapPropertiesTestJP_Arrhenius(gam, inputV0, inputVDC, params, Nr, iSample):
-    """Sets non-dimensional properties corresponding to Liu 2014 paper.
-
-    Inputs:
-      gam : Secondary electron emission coefficient
-      params : chebSolver.modelParams class
-
-    Outputs: None
-      params data is overwritten using values from Liu 2014.
-    """
-    ###################################################################
-    # User specified parameters (you may change these if you wish to
-    # run a different scenario from Liu 2014)
-    ###################################################################
-
-    # densities
-    nAr = 3.22e22     # background number density of Ar [1/m^3] (corresponds to p=100 mTorr)
-    np0 = 8e16        # "nominal" electron density [1/m^3]
-
-    # masses
-    # me = 9.10938356e-31        # mass of an electron [kg]
-    # me = 5.489e-4              # mass of an electron [u]
-    me = 0.511e6                 # mass of an electron [eV/c2]
-    # mAr = 39.948               # mass of an argon atom [u]
-    # mAr = 39.948 * 1.66054e-27 # mass of an argon atom [kg]
-    mAr = 37.2158e9              # mass of an electron [eV/c2]
-    # u = 931.4941e6             # eV/c2
-    c = 299792458                # speed of light [m/s]
-    se = 40                      # momentum cross section [A^2]
-
-    # nominal electron energy
-    e0 = 1.0  # [eV]
-
-    # pressure
-    p  = 133.3224*1.5      # [J/m^3] *1.5 to convert it to energy (1 Torr)
-
-    # gas energy at the wall
-    Tg0 = 0.038778    # 3/2*300K*kB ~ (p0 - nT[:,0])/ntot
-
-    # characteristics of driving voltage
-    V0  = inputV0                 # amplitude of driving voltage [V]
-    verticalShift = inputVDC      # DC voltage (vertical shift in driving voltage)
-    tau = (1./13.56e6)             # period of driving voltage [s]
-    L   = 2.00*0.005              # half-gap-width [m] (gap width is 2 cm)
-    electrodeArea = np.pi*0.05**2 # electrode area [m^2] (electrode diameter = 0.1 m)
-
-    # transport parameters
-    nmue = 9.66e21   # argon number density times electron mobility [1/(V*cm*s)]
-    #nmui = 4.65e19   # argon number density times ion mobility [1/(V*cm*s)]
-    #nmum = 1 / (np.sqrt(16.0 * (mAr + mAr) * 300 * 8.62e-5 * c**2
-    #                    / (3.0 * np.pi * mAr * mAr)) * se * mAr * 1.6e-19 / c**2)
-    nmum = 0.0
-    nmui = 8.0e19
-    #nmum = 9.35e19
-    nDe  = 3.86e22   # argon number density times electron diffusivity [1/(cm*s)]
-    nDi  = 2.07e18   # argon number density times ion diffusivity [1/(cm*s)]
-    nDm  = 2.42e18   # argon number density times metastable diffusivity [1/(cm*s)]
-    #nDm  = 3.914e20
-
-    # reaction parameters (NB: k_i = Ck*Ee^B*exp(-A/Ee))
-    #                          Ee = 3/2*Te (Te in eV)
-    #                          -> k_i = [Ck*(2/3)^B] * Ee^B * exp[-(3/2)*A/Ee]
-    # nominal
-    Ck = np.array([1.235e-7,3.712e-8,2.05e-7,4.0e-13,5.0e-10,4.3e-10,2.1e-15,10.0,5.0e-27]) # pre-exponential factors [cm^3/s]
-    B  = np.array([0.0,0.0,0.0,-0.5,0.0,0.74,0.0,0.0,-4.5])
-    A  = np.array([18.687,15.06,4.95,0.0,0.0,0.0,0.0,0.0,0.0]) # activation temperature [eV]
-    dH = np.array([15.76,11.56,4.2,0.0,-7.56,-11.56,-11.56,0.0,-4.2]) # energy lost per electron due to ionization rxn [eV]
-    dEps = np.array([0.0,15.76,11.56,0.0])
-
-    # BC parameters
-    # ks = 1.19e7  # electron recombination rate [cm/s]
-    ks = 1.366109824889323e7 # electron recombination rate [cm/s/eV]
-
-    ###################################################################
-    # Constants of nature (probably shouldn't change unless you have
-    # root privileges on universe)
-    ###################################################################
-    qe   = 1.6e-19   # unit charge [C]
-    eps0 = 8.86e-12  # permittivity of free space [F/m]
-    kB   = 1.38e-23  # Boltzmann constant [J/K]
-    # kB   = 8.62e−5 # Boltzmann constant [eV/K]
-
-
-    ###################################################################
-    # Calculate non-dimensional parameters
-    ###################################################################
-
-    # 1) Convert input units to base SI (except eV)
-    nDe  *= 100. # 1/(m*s)
-    nDi  *= 100. # 1/(m*s)
-    nDm  *= 100.
-    nmue *= 100. # 1/(V*m*s)
-    nmui *= 100. # 1/(V*m*s)
-    Ck[0:7] *= 1e-6 # m^3/s
-    Ck[7] *= 1 # 1/s
-    Ck[8] *= 1e-12 # m^6/s
-    # Ck[7] *= 1e-6 # Ck[7] is now in m^6/s
-    ks   *= 0.01 # m/s
-    se   *= 1.0e-20  # m^2
-
-    # 2) Compute "raw" transport parameters
-    De  = nDe/nAr
-    Di  = nDi/nAr
-    Dm  = nDm/nAr
-
-    mue = nmue/nAr
-    mui = nmui/nAr
-    mum = nmum/nAr
-
-    # 3) Compute non-dimensional properties required by solver
-    De    = De*tau/(L*L)
-    Di    = Di*tau/(L*L)
-    Dm    = Dm*tau/(L*L)
-
-    mue   = mue*V0*tau/(L*L)
-    mui   = mui*V0*tau/(L*L)
-    mum   = mum*V0*tau/(L*L)
-
-    Ck[0:2] = Ck[0:2]*tau*nAr
-    #Ck[2:5] = Ck[2:5]*tau*np0
-    Ck[2:6] = Ck[2:6]*tau*np0
-    Ck[6] *= tau*nAr
-    Ck[7] *= tau
-    Ck[8] *= tau*np0*np0
-    # Ck[7]  *= tau*nAr*nAr
-    A       = A*1.5/e0  # 1.5 to convert from temperature to energy
-    dH      = dH/e0
-    qStar   = V0/e0 # qe*V0/e0, since e0 in eV, need qe*V0 in eV, which is just V0 in V
-    alpha   = qe*np0*L*L/(V0*eps0)
-    ks      = ks*tau/L
-    p0      = p/qe/np0
-    kappaB  = 4.878171165833662*1.6129 # non-dimensional thermal conductivity of background specie
-                                # (2/3)*tau/L**2*Kb/np0/kB,
-                                # where Kb is the thermal conductivity of background specie
-
-    #params.beta = np.array([[2,2,2,1,1],[1,0,1,0,0],[0,1,0,0,0],[0,0,0,1,1]])
-    #params.alfa = np.array([[1,1,1,1,1],[0,0,0,0,0],[0,0,1,1,1],[1,1,0,0,0]])
-    #params.beta = np.array([[2,1,2,1],[1,0,1,0],[0,1,0,0],[0,0,0,1]], dtype=np.int)
-    #params.alfa = np.array([[1,1,1,1],[0,0,0,0],[0,0,1,1],[1,1,0,0]], dtype=np.int)
-    # params.beta = np.array([[2,1,2,1,1,1,0,0],[1,0,1,0,0,1,0,0],[0,1,0,0,0,0,0,0],[0,0,0,1,0,1,2,1]], dtype=np.int64)
-    # params.alfa = np.array([[1,1,1,1,1,0,0,0],[0,0,0,0,0,0,0,0],[0,0,1,1,1,2,1,1],[1,1,0,0,0,0,1,2]], dtype=np.int64)
-    params.beta = np.array([[2,1,2,0,1,1,0,0,1],[1,0,1,0,1,0,0,0,0],[0,1,0,1,0,0,0,0,1],[0,0,0,0,1,1,2,1,0]], dtype=np.int64)
-    params.alfa = np.array([[1,1,1,1,0,1,0,0,2],[0,0,0,1,0,0,0,0,1],[0,0,1,0,2,1,1,1,0],[1,1,0,0,0,0,1,0,0]], dtype=np.int64)
-	# Rxn1:  E + AR   ->  2E  +  AR+
-	# Rxn2:  E + AR   ->  E   +  AR*
-	# Rxn3:  E + AR*  ->  2E  +  AR+
-	# Rxn4:  E + AR+  ->  AR*
-	# Rxn5:  2AR*     ->  E   +  AR+  +  AR
-	# Rxn6:  E + AR*  ->  E   +  AR
-	# Rxn7:  AR* + AR ->  2AR
-	# Rxn8:  AR*      ->  AR
-	# Rxn9:  2E + AR+ ->  E   +  AR*
-
-    # 4) Set values in params class
-    params.D[0]    = De
-    params.D[1]    = Di
-    params.D[2]    = Dm
-
-    params.mu[0]   = mue
-    params.mu[1]   = mui
-    params.mu[2]   = mum
-
-    params.A[:]    = Ck[:]
-    params.B[:]    = B[:]
-    params.C[:]    = A[:]
-
-    # Account for the 2/3 term to convert from electron temperature to electron energy
-    for i in range(len(params.A)):
-        params.A[i] *= (2/3)**(params.B[i])
-
-    params.dH[:]   = dH[:]
-    params.dEps[:] = dEps[:]
-    params.qStar   = qStar
-    params.alpha   = alpha
-    params.ks      = ks
-    params.gam     = gam
-    params.kappaB  = kappaB
-    params.nAronp0 = nAr / np0
-    params.p0      = p0
-    params.Tg0     = Tg0
-    params.EC      = 2.0 * me / mAr \
-        * np.sqrt(16.0 * (me + mAr) * e0 * c**2
-                  / (3.0 * np.pi * me * mAr)) * se * nAr * tau
-    # params.EC = 2.0 * me / mAr * 3.8e9 * tau
-
-    params.verticalShift = verticalShift / V0
-
-    # Parameters needed to compute the current with dimensions
-    params.V0Ltau  = V0 / (L * tau)
-    params.V0L     = V0 / L
-    params.LLV0tau = (L*L) / (V0*tau)
-    params.tauL    = L / tau
-    params.np0     = np0           # "nominal" electron density [1/m^3]
-    params.qe      = qe            # unit charge [C]
-    params.eps0    = eps0          # unit charge [C]
-    params.eArea   = electrodeArea # electrode area [m^2]
-
-    # reactionExpressionslist = [f"{params.A[0]} * energy**{params.B[0]} * np.exp(-{params.C[0]} / energy)",
-    #                            f"{params.A[1]} * energy**{params.B[1]} * np.exp(-{params.C[1]} / energy)",
-    #                            f"{params.A[2]} * energy**{params.B[2]} * np.exp(-{params.C[2]} / energy)",
-    #                            f"{params.A[3]} * energy**{params.B[3]} * np.exp(-{params.C[3]} / energy)",
-    #                            f"{params.A[4]} * energy**{params.B[4]} * np.exp(-{params.C[4]} / energy)",
-    #                            f"{params.A[5]} * energy**{params.B[5]} * np.exp(-{params.C[5]} / energy)",
-    #                            f"{params.A[6]} * energy**{params.B[6]} * np.exp(-{params.C[6]} / energy)",
-    #                            f"{params.A[7]} * energy**{params.B[7]} * np.exp(-{params.C[7]} / energy)"]
-
-    # reactionTExpressionslist = [f"{params.A[0]} * (energy**({params.B[0]}-1)) * np.exp(-{params.C[0]}/energy) * ({params.B[0]} + {params.C[0]}/energy)",
-    #                             f"{params.A[1]} * (energy**({params.B[1]}-1)) * np.exp(-{params.C[1]}/energy) * ({params.B[1]} + {params.C[1]}/energy)",
-    #                             f"{params.A[2]} * (energy**({params.B[2]}-1)) * np.exp(-{params.C[2]}/energy) * ({params.B[2]} + {params.C[2]}/energy)",
-    #                             f"{params.A[3]} * (energy**({params.B[3]}-1)) * np.exp(-{params.C[3]}/energy) * ({params.B[3]} + {params.C[3]}/energy)",
-    #                             f"{params.A[4]} * (energy**({params.B[4]}-1)) * np.exp(-{params.C[4]}/energy) * ({params.B[4]} + {params.C[4]}/energy)",
-    #                             f"{params.A[5]} * (energy**({params.B[5]}-1)) * np.exp(-{params.C[5]}/energy) * ({params.B[5]} + {params.C[5]}/energy)",
-    #                             f"{params.A[6]} * (energy**({params.B[6]}-1)) * np.exp(-{params.C[6]}/energy) * ({params.B[6]} + {params.C[6]}/energy)",
-    #                             f"{params.A[7]} * (energy**({params.B[7]}-1)) * np.exp(-{params.C[7]}/energy) * ({params.B[7]} + {params.C[7]}/energy)"]
-
-    reactionExpressionslist = [f"{params.A[0]} * energy**{params.B[0]} * np.exp(-{params.C[0]} / energy)",
-                                f"{params.A[1]} * energy**{params.B[1]} * np.exp(-{params.C[1]} / energy)",
-                                f"{params.A[2]} * energy**{params.B[2]} * np.exp(-{params.C[2]} / energy)",
-                                f"{params.A[3]} * energy**{params.B[3]} * np.exp(-{params.C[3]} / energy)",
-                                f"{params.A[4]} * energy**{params.B[4]} * np.exp(-{params.C[4]} / energy)",
-                                f"{params.A[5]} * energy**{params.B[5]} * np.exp(-{params.C[5]} / energy)",
-                                f"{params.A[6]} * energy**{params.B[6]} * np.exp(-{params.C[6]} / energy)",
-				f"{params.A[7]} * energy**{params.B[7]} * np.exp(-{params.C[7]} / energy)",
-				f"{params.A[8]} * energy**{params.B[8]} * np.exp(-{params.C[8]} / energy)"]
-
-    reactionTExpressionslist = [f"{params.A[0]} * (energy**({params.B[0]}-1)) * np.exp(-{params.C[0]}/energy) * ({params.B[0]} + {params.C[0]}/energy)",
-                                f"{params.A[1]} * (energy**({params.B[1]}-1)) * np.exp(-{params.C[1]}/energy) * ({params.B[1]} + {params.C[1]}/energy)",
-                                f"{params.A[2]} * (energy**({params.B[2]}-1)) * np.exp(-{params.C[2]}/energy) * ({params.B[2]} + {params.C[2]}/energy)",
-                                f"{params.A[3]} * (energy**({params.B[3]}-1)) * np.exp(-{params.C[3]}/energy) * ({params.B[3]} + {params.C[3]}/energy)",
-                                f"{params.A[4]} * (energy**({params.B[4]}-1)) * np.exp(-{params.C[4]}/energy) * ({params.B[4]} + {params.C[4]}/energy)",
-                                f"{params.A[5]} * (energy**({params.B[5]}-1)) * np.exp(-{params.C[5]}/energy) * ({params.B[5]} + {params.C[5]}/energy)",
-                                f"{params.A[6]} * (energy**({params.B[6]}-1)) * np.exp(-{params.C[6]}/energy) * ({params.B[6]} + {params.C[6]}/energy)",
-				f"{params.A[7]} * (energy**({params.B[7]}-1)) * np.exp(-{params.C[7]}/energy) * ({params.B[7]} + {params.C[7]}/energy)",
-				f"{params.A[8]} * (energy**({params.B[8]}-1)) * np.exp(-{params.C[8]}/energy) * ({params.B[8]} + {params.C[8]}/energy)"]
-
-    reactionExpressionTypelist =  np.array([False,False,False,False,False,False,False,False,False])
-
-    reactionsList = []
-    LOGFilename = 'interpolationSample%s.log'%str(iSample)
-    f = open(LOGFilename, 'w')
-	# import h5py as h5
-	# rxnName = ["Ionization", ...] <- dictionary containing reaction name root string
-	# for r in range(Nr):
-	#	if reactionExpressionTypeList[r]:
-	#		fileName = "{0:s}.{1:08d}.h5".format(rxnName[r], iSample)
-	#		f = h5.File(filename, "r")
-	#		D = f["table"]
-
-    for i in range(Nr):
-        if reactionExpressionTypelist[i]:
-            Nsample = 1
-            N300 = 200
-
-            root_dir = ".."
-            rate_file = open("{0:s}/BOLSIGChemistry_NominalRates/reaction300K_{1:d}.txt".format(root_dir, i), 'r')
-            temp_file = open("{0:s}/BOLSIGChemistry_NominalRates/reaction300K_Te.txt".format(root_dir), 'r')
-            
-            #rateCoeff = np.fromfile(rate_file)
-            rateCoeff = np.genfromtxt(rate_file)
-            rate_file.close()
-            rateCoeff = np.reshape(rateCoeff,[Nsample, N300]).T[:,iSample]
-
-            #Te = np.fromfile(temp_file)
-            Te = np.genfromtxt(temp_file)
-            temp_file.close()
-            Te = np.reshape(Te,[Nsample, N300]).T[:,iSample]
-
-            # Sorting mean energy array and rate coefficient array based on
-            # the mean energy array.
-            Teinds = Te.argsort()
-            rateCoeff = rateCoeff[Teinds]
-            Te = Te[Teinds]
-
-            # Find duplicates
-            TeDuplicateinds = np.where(np.abs(np.diff(Te, axis=0)) > 0.0)
-            TeDuplicateindsForLog = np.where(np.abs(np.diff(Te, axis=0)) == 0.0)
-            rateCoeff = rateCoeff[TeDuplicateinds]
-            Te = Te[TeDuplicateinds]
-
-            # Nondimensionalization of mean energy.
-            Te *= 1.5
-
-            # Find first non-zero value of the coefficient rate.
-            I = np.nonzero(rateCoeff)
-
-            diffRateCoeff = [j-i for i, j in zip(rateCoeff[:-1], rateCoeff[1:])]
-            diffTe = [j-i for i, j in zip(Te[:-1], Te[1:])]
-
-            Monotonicity = np.asarray([j/i for i, j in zip(diffTe, diffRateCoeff)])
-            Monotonicity = np.insert(Monotonicity, 0, 0.0, axis=0)
-
-            Nan = np.isnan(Monotonicity)
-            Inf = np.isinf(Monotonicity)
-            indexPositive = np.where(Monotonicity>0.0)
-            Positive = np.full(Monotonicity.shape, False, dtype=bool)
-            Positive[indexPositive] = True
-
-            indices = Nan + Inf + Positive
-
-            lastFalse = np.where(indices==False)[-1][-1] + 2
-
-            # Transformation to log scale.
-            TeLog = np.log(Te)
-
-            # Compute the slope of the rate coefficient between its first two non-zero values.
-            # Finite differences are used.
-            dydx = (rateCoeff[lastFalse + 1] - rateCoeff[lastFalse]) \
-                 / (Te[lastFalse + 1] - Te[lastFalse])
-
-            # Arrhenius form: kf = A * exp(-C / Te)
-            C = Te[lastFalse]**2.0*dydx / rateCoeff[lastFalse]
-
-            # Compute pre-exponential coefficient, A, in log scale.
-            ALog = np.log(rateCoeff[lastFalse]) + C / Te[lastFalse]
-
-            # Transform rate coefficient in log scale.
-            rateCoeffLog = np.zeros(rateCoeff.shape)
-            rateCoeffLog[lastFalse:] = np.log(rateCoeff[lastFalse:])
-            # For the troublesome values, we use the Arrhenius form.
-            rateCoeffLog[0:lastFalse] = ALog - C / Te[0:lastFalse]
-            # Nondimensionalization in log scale.
-            if i < 2:
-                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
-            else:
-                rateCoeffLog += - np.log(1.0/tau) + np.log(np0)
-
-            # Interpolation in log scale.
-            reactionExpressionsLog = CubicSpline(TeLog, rateCoeffLog)
-            # Gradient in log scale
-            reactionTExpressionsLog = CubicSpline.derivative(reactionExpressionsLog)
-
-            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
-                                rxnBolsig = reactionExpressionTypelist[i],
-                                kf_log = reactionExpressionsLog,
-                                kf_T_log = reactionTExpressionsLog)
-            reactionsList.append(reaction)
-
-            logging.basicConfig(filename=LOGFilename)
-            logging.warning('Interpolation info (Reaction %s):', i)
-            logging.warning('First non-zero entry in the rate coefficient: %s', I[0][0])
-            logging.warning('Monotonicity of the rate coefficient start from entry: %s', lastFalse)
-            logging.warning('Position of possible duplicates in mean energy array: %s',
-                          TeDuplicateindsForLog[0])
-
-        else:
-            rxn   = eval("lambda energy :" + reactionExpressionslist[i])
-            rxn_T = eval("lambda energy :" + reactionTExpressionslist[i])
-
-            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
-                                rxnBolsig = reactionExpressionTypelist[i],
-                                kf = rxn, kf_T = rxn_T)
-            reactionsList.append(reaction)
-
-    params.reactionsList = reactionsList
-
-    # 5) Dump to screen
-    params.print()
diff --git a/psaapPropertiesTestJP_Nominal.py b/psaapPropertiesTestJP_Nominal.py
deleted file mode 100755
index 8b8ce532b..000000000
--- a/psaapPropertiesTestJP_Nominal.py
+++ /dev/null
@@ -1,371 +0,0 @@
-import numpy as np
-from scipy.interpolate import CubicSpline
-import csv
-import matplotlib.pyplot as plt
-import matplotlib.colors as mcolors
-
-import logging
-
-class Reaction(object):
-    def __init__(self, *initial_data, **kwargs):
-        for dictionary in initial_data:
-            for key in dictionary:
-                setattr(self, key, dictionary[key])
-        for key in kwargs:
-            setattr(self, key, kwargs[key])
-
-def setPsaapPropertiesTestJP_Nominal(gam, inputV0, inputVDC, params, Nr, iSample):
-    """Sets non-dimensional properties corresponding to Liu 2014 paper.
-
-    Inputs:
-      gam : Secondary electron emission coefficient
-      params : chebSolver.modelParams class
-
-    Outputs: None
-      params data is overwritten using values from Liu 2014.
-    """
-    ###################################################################
-    # User specified parameters (you may change these if you wish to
-    # run a different scenario from Liu 2014)
-    ###################################################################
-
-    # densities
-    nAr = 3.22e22     # background number density of Ar [1/m^3] (corresponds to p=100 mTorr)
-    np0 = 8e16        # "nominal" electron density [1/m^3]
-
-    # masses
-    # me = 9.10938356e-31        # mass of an electron [kg]
-    # me = 5.489e-4              # mass of an electron [u]
-    me = 0.511e6                 # mass of an electron [eV/c2]
-    # mAr = 39.948               # mass of an argon atom [u]
-    # mAr = 39.948 * 1.66054e-27 # mass of an argon atom [kg]
-    mAr = 37.2158e9              # mass of an electron [eV/c2]
-    # u = 931.4941e6             # eV/c2
-    c = 299792458                # speed of light [m/s]
-    se = 40                      # momentum cross section [A^2]
-
-    # nominal electron energy
-    e0 = 1.0  # [eV]
-
-    # pressure
-    p  = 133.3224*1.5      # [J/m^3] *1.5 to convert it to energy (1 Torr)
-
-    # gas energy at the wall
-    Tg0 = 0.038778    # 3/2*300K*kB ~ (p0 - nT[:,0])/ntot
-
-    # characteristics of driving voltage
-    V0  = inputV0                 # amplitude of driving voltage [V]
-    verticalShift = inputVDC      # DC voltage (vertical shift in driving voltage)
-    tau = (1./13.56e6)             # period of driving voltage [s]
-    L   = 2.00*0.005              # half-gap-width [m] (gap width is 2 cm)
-    electrodeArea = np.pi*0.05**2 # electrode area [m^2] (electrode diameter = 0.1 m)
-
-    # transport parameters
-    nmue = 9.66e21   # argon number density times electron mobility [1/(V*cm*s)]
-    #nmui = 4.65e19   # argon number density times ion mobility [1/(V*cm*s)]
-    #nmum = 1 / (np.sqrt(16.0 * (mAr + mAr) * 300 * 8.62e-5 * c**2
-    #                    / (3.0 * np.pi * mAr * mAr)) * se * mAr * 1.6e-19 / c**2)
-    nmum = 0.0
-    nmui = 8.0e19
-    #nmum = 9.35e19
-    nDe  = 3.86e22   # argon number density times electron diffusivity [1/(cm*s)]
-    nDi  = 2.07e18   # argon number density times ion diffusivity [1/(cm*s)]
-    nDm  = 2.42e18   # argon number density times metastable diffusivity [1/(cm*s)]
-    #nDm  = 3.914e20
-
-    # reaction parameters (NB: k_i = Ck*Ee^B*exp(-A/Ee))
-    #                          Ee = 3/2*Te (Te in eV)
-    #                          -> k_i = [Ck*(2/3)^B] * Ee^B * exp[-(3/2)*A/Ee]
-    # nominal
-    Ck = np.array([1.235e-7,3.712e-8,2.05e-7,4.0e-13,5.0e-10,4.3e-10,2.1e-15,10.0,5.0e-27]) # pre-exponential factors [cm^3/s]
-    B  = np.array([0.0,0.0,0.0,-0.5,0.0,0.74,0.0,0.0,-4.5])
-    A  = np.array([18.687,15.06,4.95,0.0,0.0,0.0,0.0,0.0,0.0]) # activation temperature [eV]
-    dH = np.array([15.76,11.56,4.2,0.0,-7.56,-11.56,-11.56,0.0,-4.2]) # energy lost per electron due to ionization rxn [eV]
-    dEps = np.array([0.0,15.76,11.56,0.0])
-
-    # BC parameters
-    # ks = 1.19e7  # electron recombination rate [cm/s]
-    ks = 1.366109824889323e7 # electron recombination rate [cm/s/eV]
-
-    ###################################################################
-    # Constants of nature (probably shouldn't change unless you have
-    # root privileges on universe)
-    ###################################################################
-    qe   = 1.6e-19   # unit charge [C]
-    eps0 = 8.86e-12  # permittivity of free space [F/m]
-    kB   = 1.38e-23  # Boltzmann constant [J/K]
-    # kB   = 8.62e−5 # Boltzmann constant [eV/K]
-
-
-    ###################################################################
-    # Calculate non-dimensional parameters
-    ###################################################################
-
-    # 1) Convert input units to base SI (except eV)
-    nDe  *= 100. # 1/(m*s)
-    nDi  *= 100. # 1/(m*s)
-    nDm  *= 100.
-    nmue *= 100. # 1/(V*m*s)
-    nmui *= 100. # 1/(V*m*s)
-    Ck[0:7] *= 1e-6 # m^3/s
-    Ck[7] *= 1 # 1/s
-    Ck[8] *= 1e-12 # m^6/s
-    # Ck[7] *= 1e-6 # Ck[7] is now in m^6/s
-    ks   *= 0.01 # m/s
-    se   *= 1.0e-20  # m^2
-
-    # 2) Compute "raw" transport parameters
-    De  = nDe/nAr
-    Di  = nDi/nAr
-    Dm  = nDm/nAr
-
-    mue = nmue/nAr
-    mui = nmui/nAr
-    mum = nmum/nAr
-
-    # 3) Compute non-dimensional properties required by solver
-    De    = De*tau/(L*L)
-    Di    = Di*tau/(L*L)
-    Dm    = Dm*tau/(L*L)
-
-    mue   = mue*V0*tau/(L*L)
-    mui   = mui*V0*tau/(L*L)
-    mum   = mum*V0*tau/(L*L)
-
-    Ck[0:2] = Ck[0:2]*tau*nAr
-    Ck[2:6] = Ck[2:6]*tau*np0
-    Ck[6] *= tau*nAr
-    Ck[7] *= tau
-    Ck[8] *= tau*np0*np0
-    A       = A*1.5/e0  # 1.5 to convert from temperature to energy
-    dH      = dH/e0
-    qStar   = V0/e0 # qe*V0/e0, since e0 in eV, need qe*V0 in eV, which is just V0 in V
-    alpha   = qe*np0*L*L/(V0*eps0)
-    ks      = ks*tau/L
-    p0      = p/qe/np0
-    kappaB  = 4.878171165833662*1.6129 # non-dimensional thermal conductivity of background specie
-                                # (2/3)*tau/L**2*Kb/np0/kB,
-                                # where Kb is the thermal conductivity of background specie
-
-    #params.beta = np.array([[2,2,2,1,1],[1,0,1,0,0],[0,1,0,0,0],[0,0,0,1,1]])
-    #params.alfa = np.array([[1,1,1,1,1],[0,0,0,0,0],[0,0,1,1,1],[1,1,0,0,0]])
-    #params.beta = np.array([[2,1,2,1],[1,0,1,0],[0,1,0,0],[0,0,0,1]], dtype=np.int)
-    #params.alfa = np.array([[1,1,1,1],[0,0,0,0],[0,0,1,1],[1,1,0,0]], dtype=np.int)
-    # params.beta = np.array([[2,1,2,1,1,1,0,0],[1,0,1,0,0,1,0,0],[0,1,0,0,0,0,0,0],[0,0,0,1,0,1,2,1]], dtype=np.int64)
-    # params.alfa = np.array([[1,1,1,1,1,0,0,0],[0,0,0,0,0,0,0,0],[0,0,1,1,1,2,1,1],[1,1,0,0,0,0,1,2]], dtype=np.int64)
-    params.beta = np.array([[2,1,2,0,1,1,0,0,1],[1,0,1,0,1,0,0,0,0],[0,1,0,1,0,0,0,0,1],[0,0,0,0,1,1,2,1,0]], dtype=np.int64)
-    params.alfa = np.array([[1,1,1,1,0,1,0,0,2],[0,0,0,1,0,0,0,0,1],[0,0,1,0,2,1,1,1,0],[1,1,0,0,0,0,1,0,0]], dtype=np.int64)
-	# Rxn1:  E + AR   ->  2E  +  AR+
-	# Rxn2:  E + AR   ->  E   +  AR*
-	# Rxn3:  E + AR*  ->  2E  +  AR+
-	# Rxn4:  E + AR+  ->  AR*
-	# Rxn5:  2AR*     ->  E   +  AR+  +  AR
-	# Rxn6:  E + AR*  ->  E   +  AR
-	# Rxn7:  AR* + AR ->  2AR
-	# Rxn8:  AR*      ->  AR
-	# Rxn9:  2E + AR+ ->  E   +  AR*
-
-    # 4) Set values in params class
-    params.D[0]    = De
-    params.D[1]    = Di
-    params.D[2]    = Dm
-
-    params.mu[0]   = mue
-    params.mu[1]   = mui
-    params.mu[2]   = mum
-
-    params.A[:]    = Ck[:]
-    params.B[:]    = B[:]
-    params.C[:]    = A[:]
-
-    # Account for the 2/3 term to convert from electron temperature to electron energy
-    for i in range(len(params.A)):
-        params.A[i] *= (2/3)**(params.B[i])
-
-    params.dH[:]   = dH[:]
-    params.dEps[:] = dEps[:]
-    params.qStar   = qStar
-    params.alpha   = alpha
-    params.ks      = ks
-    params.gam     = gam
-    params.kappaB  = kappaB
-    params.nAronp0 = nAr / np0
-    params.p0      = p0
-    params.Tg0     = Tg0
-    params.EC      = 2.0 * me / mAr \
-        * np.sqrt(16.0 * (me + mAr) * e0 * c**2
-                  / (3.0 * np.pi * me * mAr)) * se * nAr * tau
-    # params.EC = 2.0 * me / mAr * 3.8e9 * tau
-
-    params.verticalShift = verticalShift / V0
-
-    # Parameters needed to compute the current with dimensions
-    params.V0Ltau  = V0 / (L * tau)
-    params.V0L     = V0 / L
-    params.LLV0tau = (L*L) / (V0*tau)
-    params.tauL    = L / tau
-    params.np0     = np0           # "nominal" electron density [1/m^3]
-    params.qe      = qe            # unit charge [C]
-    params.eps0    = eps0          # unit charge [C]
-    params.eArea   = electrodeArea # electrode area [m^2]
-
-    # reactionExpressionslist = [f"{params.A[0]} * energy**{params.B[0]} * np.exp(-{params.C[0]} / energy)",
-    #                            f"{params.A[1]} * energy**{params.B[1]} * np.exp(-{params.C[1]} / energy)",
-    #                            f"{params.A[2]} * energy**{params.B[2]} * np.exp(-{params.C[2]} / energy)",
-    #                            f"{params.A[3]} * energy**{params.B[3]} * np.exp(-{params.C[3]} / energy)",
-    #                            f"{params.A[4]} * energy**{params.B[4]} * np.exp(-{params.C[4]} / energy)",
-    #                            f"{params.A[5]} * energy**{params.B[5]} * np.exp(-{params.C[5]} / energy)",
-    #                            f"{params.A[6]} * energy**{params.B[6]} * np.exp(-{params.C[6]} / energy)",
-    #                            f"{params.A[7]} * energy**{params.B[7]} * np.exp(-{params.C[7]} / energy)"]
-
-    # reactionTExpressionslist = [f"{params.A[0]} * (energy**({params.B[0]}-1)) * np.exp(-{params.C[0]}/energy) * ({params.B[0]} + {params.C[0]}/energy)",
-    #                             f"{params.A[1]} * (energy**({params.B[1]}-1)) * np.exp(-{params.C[1]}/energy) * ({params.B[1]} + {params.C[1]}/energy)",
-    #                             f"{params.A[2]} * (energy**({params.B[2]}-1)) * np.exp(-{params.C[2]}/energy) * ({params.B[2]} + {params.C[2]}/energy)",
-    #                             f"{params.A[3]} * (energy**({params.B[3]}-1)) * np.exp(-{params.C[3]}/energy) * ({params.B[3]} + {params.C[3]}/energy)",
-    #                             f"{params.A[4]} * (energy**({params.B[4]}-1)) * np.exp(-{params.C[4]}/energy) * ({params.B[4]} + {params.C[4]}/energy)",
-    #                             f"{params.A[5]} * (energy**({params.B[5]}-1)) * np.exp(-{params.C[5]}/energy) * ({params.B[5]} + {params.C[5]}/energy)",
-    #                             f"{params.A[6]} * (energy**({params.B[6]}-1)) * np.exp(-{params.C[6]}/energy) * ({params.B[6]} + {params.C[6]}/energy)",
-    #                             f"{params.A[7]} * (energy**({params.B[7]}-1)) * np.exp(-{params.C[7]}/energy) * ({params.B[7]} + {params.C[7]}/energy)"]
-
-    reactionExpressionslist = [f"{params.A[0]} * energy**{params.B[0]} * np.exp(-{params.C[0]} / energy)",
-                                f"{params.A[1]} * energy**{params.B[1]} * np.exp(-{params.C[1]} / energy)",
-                                f"{params.A[2]} * energy**{params.B[2]} * np.exp(-{params.C[2]} / energy)",
-                                f"{params.A[3]} * energy**{params.B[3]} * np.exp(-{params.C[3]} / energy)",
-                                f"{params.A[4]} * energy**{params.B[4]} * np.exp(-{params.C[4]} / energy)",
-                                f"{params.A[5]} * energy**{params.B[5]} * np.exp(-{params.C[5]} / energy)",
-                                f"{params.A[6]} * energy**{params.B[6]} * np.exp(-{params.C[6]} / energy)",
-				f"{params.A[7]} * energy**{params.B[7]} * np.exp(-{params.C[7]} / energy)",
-				f"{params.A[8]} * energy**{params.B[8]} * np.exp(-{params.C[8]} / energy)"]
-
-    reactionTExpressionslist = [f"{params.A[0]} * (energy**({params.B[0]}-1)) * np.exp(-{params.C[0]}/energy) * ({params.B[0]} + {params.C[0]}/energy)",
-                                f"{params.A[1]} * (energy**({params.B[1]}-1)) * np.exp(-{params.C[1]}/energy) * ({params.B[1]} + {params.C[1]}/energy)",
-                                f"{params.A[2]} * (energy**({params.B[2]}-1)) * np.exp(-{params.C[2]}/energy) * ({params.B[2]} + {params.C[2]}/energy)",
-                                f"{params.A[3]} * (energy**({params.B[3]}-1)) * np.exp(-{params.C[3]}/energy) * ({params.B[3]} + {params.C[3]}/energy)",
-                                f"{params.A[4]} * (energy**({params.B[4]}-1)) * np.exp(-{params.C[4]}/energy) * ({params.B[4]} + {params.C[4]}/energy)",
-                                f"{params.A[5]} * (energy**({params.B[5]}-1)) * np.exp(-{params.C[5]}/energy) * ({params.B[5]} + {params.C[5]}/energy)",
-                                f"{params.A[6]} * (energy**({params.B[6]}-1)) * np.exp(-{params.C[6]}/energy) * ({params.B[6]} + {params.C[6]}/energy)",
-				f"{params.A[7]} * (energy**({params.B[7]}-1)) * np.exp(-{params.C[7]}/energy) * ({params.B[7]} + {params.C[7]}/energy)",
-				f"{params.A[8]} * (energy**({params.B[8]}-1)) * np.exp(-{params.C[8]}/energy) * ({params.B[8]} + {params.C[8]}/energy)"]
-
-    reactionExpressionTypelist =  np.array([True,True,True,False,False,False,False,False,False])
-
-    reactionsList = []
-    LOGFilename = 'interpolationSample%s.log'%str(iSample)
-    f = open(LOGFilename, 'w')
-	# import h5py as h5
-	# rxnName = ["Ionization", ...] <- dictionary containing reaction name root string
-	# for r in range(Nr):
-	#	if reactionExpressionTypeList[r]:
-	#		fileName = "{0:s}.{1:08d}.h5".format(rxnName[r], iSample)
-	#		f = h5.File(filename, "r")
-	#		D = f["table"]
-
-    for i in range(Nr):
-        if reactionExpressionTypelist[i]:
-            Nsample = 1
-            N300 = 200
-
-            root_dir = ".."
-            rate_file = open("{0:s}/BOLSIGChemistry_NominalRates/reaction300K_{1:d}.txt".format(root_dir, i), 'r')
-            temp_file = open("{0:s}/BOLSIGChemistry_NominalRates/reaction300K_Te.txt".format(root_dir), 'r')
-            
-            #rateCoeff = np.fromfile(rate_file)
-            rateCoeff = np.genfromtxt(rate_file)
-            rate_file.close()
-            rateCoeff = np.reshape(rateCoeff,[Nsample, N300]).T[:,iSample]
-
-            #Te = np.fromfile(temp_file)
-            Te = np.genfromtxt(temp_file)
-            temp_file.close()
-            Te = np.reshape(Te,[Nsample, N300]).T[:,iSample]
-
-            # Sorting mean energy array and rate coefficient array based on
-            # the mean energy array.
-            Teinds = Te.argsort()
-            rateCoeff = rateCoeff[Teinds]
-            Te = Te[Teinds]
-
-            # Find duplicates
-            TeDuplicateinds = np.where(np.abs(np.diff(Te, axis=0)) > 0.0)
-            TeDuplicateindsForLog = np.where(np.abs(np.diff(Te, axis=0)) == 0.0)
-            rateCoeff = rateCoeff[TeDuplicateinds]
-            Te = Te[TeDuplicateinds]
-
-            # Nondimensionalization of mean energy.
-            #Te *= 1.5
-
-            # Find first non-zero value of the coefficient rate.
-            I = np.nonzero(rateCoeff)
-
-            diffRateCoeff = [j-i for i, j in zip(rateCoeff[:-1], rateCoeff[1:])]
-            diffTe = [j-i for i, j in zip(Te[:-1], Te[1:])]
-
-            Monotonicity = np.asarray([j/i for i, j in zip(diffTe, diffRateCoeff)])
-            Monotonicity = np.insert(Monotonicity, 0, 0.0, axis=0)
-
-            Nan = np.isnan(Monotonicity)
-            Inf = np.isinf(Monotonicity)
-            indexPositive = np.where(Monotonicity>0.0)
-            Positive = np.full(Monotonicity.shape, False, dtype=bool)
-            Positive[indexPositive] = True
-
-            indices = Nan + Inf + Positive
-
-            lastFalse = np.where(indices==False)[-1][-1] + 2
-
-            # Transformation to log scale.
-            TeLog = np.log(Te)
-
-            # Compute the slope of the rate coefficient between its first two non-zero values.
-            # Finite differences are used.
-            dydx = (rateCoeff[lastFalse + 1] - rateCoeff[lastFalse]) \
-                 / (Te[lastFalse + 1] - Te[lastFalse])
-
-            # Arrhenius form: kf = A * exp(-C / Te)
-            C = Te[lastFalse]**2.0*dydx / rateCoeff[lastFalse]
-
-            # Compute pre-exponential coefficient, A, in log scale.
-            ALog = np.log(rateCoeff[lastFalse]) + C / Te[lastFalse]
-
-            # Transform rate coefficient in log scale.
-            rateCoeffLog = np.zeros(rateCoeff.shape)
-            rateCoeffLog[lastFalse:] = np.log(rateCoeff[lastFalse:])
-            # For the troublesome values, we use the Arrhenius form.
-            rateCoeffLog[0:lastFalse] = ALog - C / Te[0:lastFalse]
-            # Nondimensionalization in log scale.
-            if i < 2:
-                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
-            else:
-                rateCoeffLog += - np.log(1.0/tau) + np.log(np0)
-
-            # Interpolation in log scale.
-            reactionExpressionsLog = CubicSpline(TeLog, rateCoeffLog)
-            # Gradient in log scale
-            reactionTExpressionsLog = CubicSpline.derivative(reactionExpressionsLog)
-
-            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
-                                rxnBolsig = reactionExpressionTypelist[i],
-                                kf_log = reactionExpressionsLog,
-                                kf_T_log = reactionTExpressionsLog)
-            reactionsList.append(reaction)
-
-            logging.basicConfig(filename=LOGFilename)
-            logging.warning('Interpolation info (Reaction %s):', i)
-            logging.warning('First non-zero entry in the rate coefficient: %s', I[0][0])
-            logging.warning('Monotonicity of the rate coefficient start from entry: %s', lastFalse)
-            logging.warning('Position of possible duplicates in mean energy array: %s',
-                          TeDuplicateindsForLog[0])
-
-        else:
-            rxn   = eval("lambda energy :" + reactionExpressionslist[i])
-            rxn_T = eval("lambda energy :" + reactionTExpressionslist[i])
-
-            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
-                                rxnBolsig = reactionExpressionTypelist[i],
-                                kf = rxn, kf_T = rxn_T)
-            reactionsList.append(reaction)
-
-    params.reactionsList = reactionsList
-
-    # 5) Dump to screen
-    params.print()
diff --git a/psaapPropertiesWithSampling.py b/psaapPropertiesWithSampling.py
deleted file mode 100644
index ce6a6977f..000000000
--- a/psaapPropertiesWithSampling.py
+++ /dev/null
@@ -1,382 +0,0 @@
-import numpy as np
-from scipy.interpolate import CubicSpline
-
-#import matplotlib.pyplot as plt
-#import matplotlib.colors as mcolors
-
-import logging
-
-class Reaction(object):
-    def __init__(self, *initial_data, **kwargs):
-        for dictionary in initial_data:
-            for key in dictionary:
-                setattr(self, key, dictionary[key])
-        for key in kwargs:
-            setattr(self, key, kwargs[key])
-
-def setPsaapPropertiesWithSampling(gam, inputV0, inputVDC, params, Nr, iSample):
-    """Sets non-dimensional properties corresponding to Liu 2014 paper.
-
-    Inputs:
-      gam : Secondary electron emission coefficient
-      params : chebSolver.modelParams class
-
-    Outputs: None
-      params data is overwritten using values from Liu 2014.
-    """
-    ###################################################################
-    # User specified parameters (you may change these if you wish to
-    # run a different scenario from Liu 2014)
-    ###################################################################
-
-    # densities
-    nAr = 3.22e22     # background number density of Ar [1/m^3] (corresponds to p=100 mTorr)
-    np0 = 8e16        # "nominal" electron density [1/m^3]
-
-    # masses
-    # me = 9.10938356e-31        # mass of an electron [kg]
-    # me = 5.489e-4              # mass of an electron [u]
-    me = 0.511e6                 # mass of an electron [eV/c2]
-    # mAr = 39.948               # mass of an argon atom [u]
-    # mAr = 39.948 * 1.66054e-27 # mass of an argon atom [kg]
-    mAr = 37.2158e9              # mass of an electron [eV/c2]
-    # u = 931.4941e6             # eV/c2
-    c = 299792458                # speed of light [m/s]
-    se = 40                      # momentum cross section [m^2]
-
-    # nominal electron energy
-    e0 = 1.0  # [eV]
-
-    # pressure
-    p  = 133.3224*1.5      # [J/m^3] *1.5 to convert it to energy (1 Torr)
-
-    # gas energy at the wall
-    Tg0 = 0.038778    # 3/2*300K*kB ~ (p0 - nT[:,0])/ntot
-
-    # characteristics of driving voltage
-    V0  = inputV0                 # amplitude of driving voltage [V]
-    verticalShift = inputVDC      # DC voltage (vertical shift in driving voltage)
-    tau = (1./13.6e6)             # period of driving voltage [s]
-    L   = 2.00*0.005              # half-gap-width [m] (gap width is 2.54cm)
-    electrodeArea = np.pi*0.05**2 # electrode area [m^2] (electrode diameter = 0.1 m)
-
-    # transport parameters
-    nmue = 9.66e21   # argon number density times electron mobility [1/(V*cm*s)]
-    nmui = 4.65e19   # argon number density times ion mobility [1/(V*cm*s)]
-    nmum = 1 / (np.sqrt(16.0 * (mAr + mAr) * 300 * 8.62e-5 * c**2
-                        / (3.0 * np.pi * mAr * mAr)) * se * mAr * 1.6e-19 / c**2)
-    nDe  = 3.86e22   # argon number density times electron diffusivity [1/(cm*s)]
-    nDi  = 2.07e18   # argon number density times ion diffusivity [1/(cm*s)]
-    nDm  = 2.42e18   # argon number density times metastable diffusivity [1/(cm*s)]
-
-    # reaction parameters (NB: k_i = Ck*exp(-A/Te))
-    #Ck = np.array([1.235e-7,3.712e-8,2.05e-7,1.818e-9,2e-7]) # pre-exponential factors [cm^3/s]
-    #A  = np.array([18.687,15.06,4.95,2.14,0.0])      # activation temperature [eV]
-    #dH = np.array([15.7,11.56,4.14,-11.56,0.0])        # energy lost per electron due to ionization rxn [eV]
-
-    #Ck = np.array([1.235e-7,3.712e-8,2.05e-7,1.818e-9]) # pre-exponential factors [cm^3/s]
-    #A  = np.array([18.687,15.06,4.95,2.14])      # activation temperature [eV]
-    #dH = np.array([15.7,11.56,4.14,-11.56])        # energy lost per electron due to ionization rxn [eV]
-
-    #Ck = np.array([1.235e-7,0.0,0.0,0.0,0.0]) # pre-exponential factors [cm^3/s]
-    #A  = np.array([18.687,15.06,4.95,2.14,0.0])      # activation temperature [eV]
-    #dH = np.array([15.7,0.0,0.0,0.0,0.0])        # energy lost per electron due to ionization rxn [eV]
-
-    # nominal
-    # Ck = np.array([1.235e-7,3.712e-8,2.05e-7,1.818e-9,2e-7,6.2e-10,3.0e-15,1.1e-31]) # pre-exponential factors [cm^3/s]
-    Ck = np.array([1.235e-7,3.712e-8,2.05e-7,4.0e-13,2e-7,5.0e-10,2.5e-15]) # pre-exponential factors [cm^3/s]
-
-    # slow excitation rate
-    #Ck = np.array([1.235e-7,0.5*3.712e-8,2.05e-7,1.818e-9,2e-7,6.2e-10,3.0e-15,1.1e-31]) # pre-exponential factors [cm^3/s]
-
-    ## fast excitation rate
-    #Ck = np.array([1.235e-7,2.0*3.712e-8,2.05e-7,1.818e-9,2e-7,6.2e-10,3.0e-15,1.1e-31]) # pre-exponential factors [cm^3/s]
-
-    # A  = np.array([18.687,15.06,4.95,2.14,0.0,0.0,0.0,0.0]) # activation temperature [eV]
-    # dH = np.array([15.7,11.56,4.14,-11.56,0.0,0.0,0.0,0.0]) # energy lost per electron due to ionization rxn [eV]
-    # dEps = np.array([0.0,15.7,11.56,0.0])
-
-    A  = np.array([18.687,15.06,4.95,0.0,0.0,0.0,0.0]) # activation temperature [eV]
-    dH = np.array([15.76,11.56,4.2,0.0,-11.56,-7.56,-11.56]) # energy lost per electron due to ionization rxn [eV]
-    dEps = np.array([0.0,15.76,11.56,0.0])
-
-    # BC parameters
-    # ks = 1.19e7  # electron recombination rate [cm/s]
-    ks = 1.366109824889323e7 # electron recombination rate [cm/s/eV]
-
-    ###################################################################
-    # Constants of nature (probably shouldn't change unless you have
-    # root privileges on universe)
-    ###################################################################
-    qe   = 1.6e-19   # unit charge [C]
-    eps0 = 8.86e-12  # permittivity of free space [F/m]
-    kB   = 1.38e-23  # Boltzmann constant [J/K]
-    # kB   = 8.62e−5 # Boltzmann constant [eV/K]
-
-
-    ###################################################################
-    # Calculate non-dimensional parameters
-    ###################################################################
-
-    # 1) Convert input units to base SI (except eV)
-    nDe  *= 100. # 1/(m*s)
-    nDi  *= 100. # 1/(m*s)
-    nDm  *= 100.
-    nmue *= 100. # 1/(V*m*s)
-    nmui *= 100. # 1/(V*m*s)
-    Ck   *= 1e-6 # m^3/s
-    # Ck[7] *= 1e-6 # Ck[7] is now in m^6/s
-    ks   *= 0.01 # m/s
-    se   *= 1.0e-20  # m^2
-
-    # 2) Compute "raw" transport parameters
-    De  = nDe/nAr
-    Di  = nDi/nAr
-    Dm  = nDm/nAr
-
-    mue = nmue/nAr
-    mui = nmui/nAr
-    mum = nmum/nAr
-
-    # 3) Compute non-dimensional properties required by solver
-    De    = De*tau/(L*L)
-    Di    = Di*tau/(L*L)
-    Dm    = Dm*tau/(L*L)
-
-    mue   = mue*V0*tau/(L*L)
-    mui   = mui*V0*tau/(L*L)
-    mum   = mum*V0*tau/(L*L)
-
-    Ck[0:2] = Ck[0:2]*tau*nAr
-    #Ck[2:5] = Ck[2:5]*tau*np0
-    Ck[2:6] = Ck[2:6]*tau*np0
-    Ck[6]  *= tau*nAr
-    # Ck[7]  *= tau*nAr*nAr
-    A       = A*1.5/e0  # 1.5 to convert from temperature to energy
-    dH      = dH/e0
-    qStar   = V0/e0 # qe*V0/e0, since e0 in eV, need qe*V0 in eV, which is just V0 in V
-    alpha   = qe*np0*L*L/(V0*eps0)
-    ks      = ks*tau/L
-    p0      = p/qe/np0
-    kappaB  = 4.878171165833662*1.6129 # non-dimensional thermal conductivity of background specie
-                                # (2/3)*tau/L**2*Kb/np0/kB,
-                                # where Kb is the thermal conductivity of background specie
-
-    #params.beta = np.array([[2,2,2,1,1],[1,0,1,0,0],[0,1,0,0,0],[0,0,0,1,1]])
-    #params.alfa = np.array([[1,1,1,1,1],[0,0,0,0,0],[0,0,1,1,1],[1,1,0,0,0]])
-    #params.beta = np.array([[2,1,2,1],[1,0,1,0],[0,1,0,0],[0,0,0,1]], dtype=np.int)
-    #params.alfa = np.array([[1,1,1,1],[0,0,0,0],[0,0,1,1],[1,1,0,0]], dtype=np.int)
-    # params.beta = np.array([[2,1,2,1,1,1,0,0],[1,0,1,0,0,1,0,0],[0,1,0,0,0,0,0,0],[0,0,0,1,0,1,2,1]], dtype=np.int64)
-    # params.alfa = np.array([[1,1,1,1,1,0,0,0],[0,0,0,0,0,0,0,0],[0,0,1,1,1,2,1,1],[1,1,0,0,0,0,1,2]], dtype=np.int64)
-    params.beta = np.array([[2,1,2,0,1,1,0],[1,0,1,0,0,1,0],[0,1,0,1,0,0,0],[0,0,0,0,1,1,2]], dtype=np.int64)
-    params.alfa = np.array([[1,1,1,1,1,0,0],[0,0,0,1,0,0,0],[0,0,1,0,1,2,1],[1,1,0,0,0,0,1]], dtype=np.int64)
-
-    # 4) Set values in params class
-    params.D[0]    = De
-    params.D[1]    = Di
-    params.D[2]    = Dm
-
-    params.mu[0]   = mue
-    params.mu[1]   = mui
-    params.mu[2]   = mum
-
-    params.A[:]    = Ck[:]
-    params.B[:]    = 0.0
-    params.C[:]    = A[:]
-
-    params.dH[:]   = dH[:]
-    params.dEps[:] = dEps[:]
-    params.qStar   = qStar
-    params.alpha   = alpha
-    params.ks      = ks
-    params.gam     = gam
-    params.kappaB  = kappaB
-    params.nAronp0 = nAr / np0
-    params.p0      = p0
-    params.Tg0     = Tg0
-    params.EC      = 2.0 * me / mAr \
-        * np.sqrt(16.0 * (me + mAr) * e0 * c**2
-                  / (3.0 * np.pi * me * mAr)) * se * nAr * tau
-    # params.EC = 2.0 * me / mAr * 3.8e9 * tau
-
-    params.verticalShift = verticalShift / V0
-
-    # Parameters needed to compute the current with dimensions
-    params.V0Ltau  = V0 / (L * tau)
-    params.V0L     = V0 / L
-    params.LLV0tau = (L*L) / (V0*tau)
-    params.tauL    = L / tau
-    params.np0     = np0           # "nominal" electron density [1/m^3]
-    params.qe      = qe            # unit charge [C]
-    params.eps0    = eps0          # unit charge [C]
-    params.eArea   = electrodeArea # electrode area [m^2]
-
-    reactionExpressionslist = [f"{params.A[0]} * energy**{params.B[0]} * np.exp(-{params.C[0]} / energy)",
-                                f"{params.A[1]} * energy**{params.B[1]} * np.exp(-{params.C[1]} / energy)",
-                                f"{params.A[2]} * energy**{params.B[2]} * np.exp(-{params.C[2]} / energy)",
-                                f"{params.A[3]} * energy**({params.B[3]}-0.5)",
-                                f"{params.A[4]} * energy**{params.B[4]} * np.exp(-{params.C[4]} / energy)",
-                                f"{params.A[5]} * energy**{params.B[5]} * np.exp(-{params.C[5]} / energy)",
-                                f"{params.A[6]} * energy**{params.B[6]} * np.exp(-{params.C[6]} / energy)"]
-
-    reactionTExpressionslist = [f"{params.A[0]} * (energy**({params.B[0]}-1)) * np.exp(-{params.C[0]}/energy) * ({params.B[0]} + {params.C[0]}/energy)",
-                                f"{params.A[1]} * (energy**({params.B[1]}-1)) * np.exp(-{params.C[1]}/energy) * ({params.B[1]} + {params.C[1]}/energy)",
-                                f"{params.A[2]} * (energy**({params.B[2]}-1)) * np.exp(-{params.C[2]}/energy) * ({params.B[2]} + {params.C[2]}/energy)",
-                                f"{params.A[3]} * (energy**({params.B[3]}-0.5-1)) * np.exp(-{params.C[3]}/energy) * ({params.B[3]}-0.5 + {params.C[3]}/energy)",
-                                f"{params.A[4]} * (energy**({params.B[4]}-1)) * np.exp(-{params.C[4]}/energy) * ({params.B[4]} + {params.C[4]}/energy)",
-                                f"{params.A[5]} * (energy**({params.B[5]}-1)) * np.exp(-{params.C[5]}/energy) * ({params.B[5]} + {params.C[5]}/energy)",
-                                f"{params.A[6]} * (energy**({params.B[6]}-1)) * np.exp(-{params.C[6]}/energy) * ({params.B[6]} + {params.C[6]}/energy)"]
-
-    reactionExpressionTypelist =  np.array([True,True,True,False,False,False,False])
-
-    reactionsList = []
-    LOGFilename = 'interpolationSample%s.log'%str(iSample)
-    f = open(LOGFilename, 'w')
-    for i in range(Nr):
-        if reactionExpressionTypelist[i]:
-            Nsample = 7200
-            N300 = 200
-
-            #rateCoeff = np.fromfile("/g/g92/jbarbere/glowDischarge/toyProblems/timeDomain/BOLSIGChemistryZeroIonDeg7200Samples/reaction300K_%s.dat" %str(i))
-            #rateCoeff = np.reshape(rateCoeff,[Nsample, N300]).T[:,iSample]
-
-            #Te = np.fromfile("/g/92/jbarbere/glowDischarge/toyProblems/timeDomain/BOLSIGChemistryZeroIonDeg7200Samples/reaction300K.Te.dat")
-            #Te = np.reshape(Te,[Nsample, N300]).T[:,iSample]
-
-            root_dir = "/g/g92/jbarbere/glowDischarge/toyProblems/timeDomain"
-            rate_file = "{0:s}/BOLSIGChemistryZeroIonDeg7200Samples/reaction300K_{1:d}.dat".format(root_dir, i)
-            temp_file = "{0:s}/BOLSIGChemistryZeroIonDeg7200Samples/reaction300K.Te.dat".format(root_dir)
-
-            rateCoeff = np.fromfile(rate_file)
-            rateCoeff = np.reshape(rateCoeff,[Nsample, N300]).T[:,iSample]
-
-            Te = np.fromfile(temp_file)
-            Te = np.reshape(Te,[Nsample, N300]).T[:,iSample]
-
-            # Sorting mean energy array and rate coefficient array based on
-            # the mean energy array.
-            Teinds = Te.argsort()
-            rateCoeff = rateCoeff[Teinds]
-            Te = Te[Teinds]
-
-            # Find duplicates
-            TeDuplicateinds = np.where(np.abs(np.diff(Te, axis=0)) > 0.0)
-            TeDuplicateindsForLog = np.where(np.abs(np.diff(Te, axis=0)) == 0.0)
-            rateCoeff = rateCoeff[TeDuplicateinds]
-            Te = Te[TeDuplicateinds]
-
-            # Nondimensionalization of mean energy.
-            Te *= 1.5
-
-            # Find first non-zero value of the coefficient rate.
-            I = np.nonzero(rateCoeff)
-
-            diffRateCoeff = [j-i for i, j in zip(rateCoeff[:-1], rateCoeff[1:])]
-            diffTe = [j-i for i, j in zip(Te[:-1], Te[1:])]
-
-            Monotonicity = np.asarray([j/i for i, j in zip(diffTe, diffRateCoeff)])
-            Monotonicity = np.insert(Monotonicity, 0, 0.0, axis=0)
-
-            Nan = np.isnan(Monotonicity)
-            Inf = np.isinf(Monotonicity)
-            indexPositive = np.where(Monotonicity>0.0)
-            Positive = np.full(Monotonicity.shape, False, dtype=bool)
-            Positive[indexPositive] = True
-
-            indices = Nan + Inf + Positive
-
-            lastFalse = np.where(indices==False)[-1][-1] + 1
-
-            # Transformation to log scale.
-            TeLog = np.log(Te)
-
-            # Find first non-zero value of the coefficient rate.
-            # I = np.nonzero(rateCoeff)
-
-            # Compute the slope of the rate coefficient between its first two non-zero values.
-            # Finite differences are used.
-            dydx = (rateCoeff[lastFalse + 1] - rateCoeff[lastFalse]) \
-                 / (Te[lastFalse + 1] - Te[lastFalse])
-
-            # Arrhenius form: kf = A * exp(-C / Te)
-            # C = Te[lastFalse]**2.0*dydx / rateCoeff[lastFalse]
-            C = (np.log(rateCoeff[lastFalse + 1]) - np.log(rateCoeff[lastFalse])) \
-              / (1.0 / Te[lastFalse] - 1.0 / Te[lastFalse + 1])
-
-            # Compute pre-exponential coefficient, A, in log scale.
-            ALog = np.log(rateCoeff[lastFalse]) + C / Te[lastFalse]
-
-            # Transform rate coefficient in log scale.
-            rateCoeffLog = np.zeros(rateCoeff.shape)
-            rateCoeffLog[lastFalse:] = np.log(rateCoeff[lastFalse:])
-            # For the troublesome values, we use the Arrhenius form.
-            rateCoeffLog[0:lastFalse] = ALog - C / Te[0:lastFalse]
-            # Nondimensionalization in log scale.
-            if i < 2:
-                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
-            else:
-                rateCoeffLog += - np.log(1.0/tau) + np.log(np0)
-
-            # Interpolation in log scale.
-            reactionExpressionsLog = CubicSpline(TeLog, rateCoeffLog)
-            # Gradient in log scale
-            reactionTExpressionsLog = CubicSpline.derivative(reactionExpressionsLog)
-
-            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
-                                rxnBolsig = reactionExpressionTypelist[i],
-                                kf_log = reactionExpressionsLog,
-                                kf_T_log = reactionTExpressionsLog)
-            reactionsList.append(reaction)
-
-            logging.basicConfig(filename=LOGFilename)
-            logging.warning('Interpolation info (Reaction %s):', i)
-            logging.warning('First non-zero entry in the rate coefficient: %s', I[0][0])
-            logging.warning('Monotonicity of the rate coefficient start from entry: %s', lastFalse)
-            logging.warning('Position of possible duplicates in mean energy array: %s',
-                          TeDuplicateindsForLog[0])
-
-            # rxn   = eval("lambda energy :" + reactionExpressionslist[i])
-            # rxn_T = eval("lambda energy :" + reactionTExpressionslist[i])
-            # # setting the axes at the centre
-            # fig ,ax = plt.subplots(figsize=(9, 6))
-            # ax.spines["top"].set_visible(True)
-            # ax.spines["right"].set_visible(True)
-            # ax.set_yscale('log')
-            # ax.set_xscale('log')
-
-            # TeFinerResolution = np.linspace(5.0e-4, 100, 20000)
-            # TeFinerResolutionLog = np.log(TeFinerResolution)
-
-            # # plot the function
-            # # plt.plot(rateCoeffXFiner, np.exp(reactionExpressions_cubicSplineDerivative_log(rateCoeffXFiner)),
-            # #  		 color='salmon', linestyle='--', label='interBolsig')
-            # plt.plot(TeFinerResolution, np.exp(reactionExpressionsLog(TeFinerResolutionLog)),
-            #  		 color='lightgreen', linestyle='--', label='interBolsig')
-            # # plt.plot(Te[:,0], reactionTExpressionsLogFiltered(TeLog[:]) * np.exp(reactionExpressionsLog(TeLog[:])) / Te[:,0],
-            # #  		 color='blue', linestyle='-', label='interBolsig')
-            # plt.plot(Te, np.exp(reactionExpressionsLog(TeLog[:])),
-            #  		 color='green', linestyle='-', label='Bolsig')
-            # plt.plot(Te, rxn(Te),
-            #  		 color='salmon', linestyle='--', label='Liu')
-            # # plt.plot(Te[:,0], rxn_T(Te[:,0]),
-            # #  		 color='red', linestyle='--', label='interBolsig')
-            # plt.xlim((0.05,100))
-            # plt.ylim((1e-50,300))
-            # plt.legend()
-            # plt.savefig("./Rates_%s.pdf" %str(i), dpi=300)
-            # # plt.xlim((-0.0001,0.0255))
-            # plt.show()
-        else:
-            rxn   = eval("lambda energy :" + reactionExpressionslist[i])
-            rxn_T = eval("lambda energy :" + reactionTExpressionslist[i])
-
-            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
-                                rxnBolsig = reactionExpressionTypelist[i],
-                                kf = rxn, kf_T = rxn_T)
-            reactionsList.append(reaction)
-
-    params.reactionsList = reactionsList
-    #params.Nr = 1
-
-    # 5) Dump to screen
-    params.print()
diff --git a/psaapProperties_4plus2Species.py b/psaapProperties_4plus2Species.py
deleted file mode 100755
index e9f41a1b5..000000000
--- a/psaapProperties_4plus2Species.py
+++ /dev/null
@@ -1,374 +0,0 @@
-import numpy as np
-from scipy.interpolate import CubicSpline
-import csv
-import matplotlib.pyplot as plt
-import matplotlib.colors as mcolors
-
-import logging
-
-class Reaction(object):
-    def __init__(self, *initial_data, **kwargs):
-        for dictionary in initial_data:
-            for key in dictionary:
-                setattr(self, key, dictionary[key])
-        for key in kwargs:
-            setattr(self, key, kwargs[key])
-
-def setPsaapProperties_4plus2Species(gam, inputV0, inputVDC, params, Nr, iSample):
-    """Sets non-dimensional properties corresponding to Liu 2014 paper.
-
-    Inputs:
-      gam : Secondary electron emission coefficient
-      params : chebSolver.modelParams class
-
-    Outputs: None
-      params data is overwritten using values from Liu 2014.
-    """
-    ###################################################################
-    # User specified parameters (you may change these if you wish to
-    # run a different scenario from Liu 2014)
-    ###################################################################
-
-    # densities
-    nAr = 3.22e22     # background number density of Ar [1/m^3] (corresponds to p=100 mTorr)
-    np0 = 8e16        # "nominal" electron density [1/m^3]
-
-    # masses
-    # me = 9.10938356e-31        # mass of an electron [kg]
-    # me = 5.489e-4              # mass of an electron [u]
-    me = 0.511e6                 # mass of an electron [eV/c2]
-    # mAr = 39.948               # mass of an argon atom [u]
-    # mAr = 39.948 * 1.66054e-27 # mass of an argon atom [kg]
-    mAr = 37.2158e9              # mass of an electron [eV/c2]
-    # u = 931.4941e6             # eV/c2
-    c = 299792458                # speed of light [m/s]
-    se = 40                      # momentum cross section [A^2]
-
-    # nominal electron energy
-    e0 = 1.0  # [eV]
-
-    # pressure
-    p  = 133.3224*1.5      # [J/m^3] *1.5 to convert it to energy (1 Torr)
-
-    # gas energy at the wall
-    Tg0 = 0.038778    # 3/2*300K*kB ~ (p0 - nT[:,0])/ntot
-
-    # characteristics of driving voltage
-    V0  = inputV0                 # amplitude of driving voltage [V]
-    verticalShift = inputVDC      # DC voltage (vertical shift in driving voltage)
-    tau = (1./13.56e6)             # period of driving voltage [s]
-    L   = 2.00*0.005              # half-gap-width [m] (gap width is 2 cm)
-    electrodeArea = np.pi*0.05**2 # electrode area [m^2] (electrode diameter = 0.1 m)
-
-    # transport parameters
-    nmue = 9.66e21   # argon number density times electron mobility [1/(V*cm*s)]
-    #nmui = 4.65e19   # argon number density times ion mobility [1/(V*cm*s)]
-    #nmum = 1 / (np.sqrt(16.0 * (mAr + mAr) * 300 * 8.62e-5 * c**2
-    #                    / (3.0 * np.pi * mAr * mAr)) * se * mAr * 1.6e-19 / c**2)
-    nmum = 0.0
-    nmur = 0.0
-    nmu4p = 0.0
-    nmui = 8.0e19
-    nDe  = 3.86e22   # argon number density times electron diffusivity [1/(cm*s)]
-    nDi  = 2.07e18   # argon number density times ion diffusivity [1/(cm*s)]
-    nDm  = 2.42e18   # argon number density times metastable diffusivity [1/(cm*s)]
-    nDr  = 2.42e18
-    nD4p = 2.42e18
-    #nDm  = 3.914e20
-
-    # reaction parameters (NB: k_i = Ck*Ee^B*exp(-A/Ee))
-    #                          Ee = 3/2*Te (Te in eV)
-    #                          -> k_i = [Ck*(2/3)^B] * Ee^B * exp[-(3/2)*A/Ee]
-    # nominal
-    Ck = np.array([1.235e-7,3.712e-8,2.05e-7,4.0e-13,5.0e-10,4.3e-10,2.1e-15,10.0,5.0e-27]) # pre-exponential factors [cm^3/s]
-    B  = np.array([0.0,0.0,0.0,-0.5,0.0,0.74,0.0,0.0,-4.5])
-    A  = np.array([18.687,15.06,4.95,0.0,0.0,0.0,0.0,0.0,0.0]) # activation temperature [eV]
-    dH = np.array([15.76,11.56,4.2,0.0,-7.56,-11.56,-11.56,0.0,-4.2]) # energy lost per electron due to ionization rxn [eV]
-    dEps = np.array([0.0,15.76,11.56,0.0,0.0,0.0])
-
-    # BC parameters
-    # ks = 1.19e7  # electron recombination rate [cm/s]
-    ks = 1.366109824889323e7 # electron recombination rate [cm/s/eV]
-
-    ###################################################################
-    # Constants of nature (probably shouldn't change unless you have
-    # root privileges on universe)
-    ###################################################################
-    qe   = 1.6e-19   # unit charge [C]
-    eps0 = 8.86e-12  # permittivity of free space [F/m]
-    kB   = 1.38e-23  # Boltzmann constant [J/K]
-    # kB   = 8.62e-5 # Boltzmann constant [eV/K]
-
-
-    ###################################################################
-    # Calculate non-dimensional parameters
-    ###################################################################
-
-    # 1) Convert input units to base SI (except eV)
-    nDe  *= 100. # 1/(m*s)
-    nDi  *= 100. # 1/(m*s)
-    nDm  *= 100.
-    nDr  *= 100.
-    nD4p *= 100.
-    nmue *= 100. # 1/(V*m*s)
-    nmui *= 100. # 1/(V*m*s)
-    Ck[0:7] *= 1e-6 # m^3/s
-    Ck[7] *= 1 # 1/s
-    Ck[8] *= 1e-12 # m^6/s
-    ks   *= 0.01 # m/s
-    se   *= 1.0e-20  # m^2
-
-    # 2) Compute "raw" transport parameters
-    De  = nDe/nAr
-    Di  = nDi/nAr
-    Dm  = nDm/nAr
-    Dr  = nDr/nAr
-    D4p = nD4p/nAr
-
-    mue = nmue/nAr
-    mui = nmui/nAr
-    mum = nmum/nAr
-    mur = nmur/nAr
-    mu4p = nmu4p/nAr
-
-    # 3) Compute non-dimensional properties required by solver
-    De    = De*tau/(L*L)
-    Di    = Di*tau/(L*L)
-    Dm    = Dm*tau/(L*L)
-    Dr    = Dr*tau/(L*L)
-    D4p   = D4p*tau/(L*L)
-
-    mue   = mue*V0*tau/(L*L)
-    mui   = mui*V0*tau/(L*L)
-    mum   = mum*V0*tau/(L*L)
-    mur   = mur*V0*tau/(L*L)
-    mu4p  = mu4p*V0*tau/(L*L)
-
-    Ck[0:2] = Ck[0:2]*tau*nAr
-    Ck[2:6] = Ck[2:6]*tau*np0
-    Ck[6] *= tau*nAr
-    Ck[7] *= tau
-    Ck[8] *= tau*np0*np0
-    A       = A*1.5/e0  # 1.5 to convert from temperature to energy
-    dH      = dH/e0
-    qStar   = V0/e0 # qe*V0/e0, since e0 in eV, need qe*V0 in eV, which is just V0 in V
-    alpha   = qe*np0*L*L/(V0*eps0)
-    ks      = ks*tau/L
-    p0      = p/qe/np0
-    kappaB  = 4.878171165833662*1.6129 # non-dimensional thermal conductivity of background specie
-                                # (2/3)*tau/L**2*Kb/np0/kB,
-                                # where Kb is the thermal conductivity of background specie
-
-    params.beta = np.array([[2,1,2,0,1,1,0,0,1],
-                            [1,0,1,0,1,0,0,0,0],
-                            [0,1,0,1,0,0,0,0,1],
-                            [0,0,0,0,0,0,0,0,0],
-                            [0,0,0,0,0,0,0,0,0],
-                            [0,0,0,0,1,1,2,1,0]], dtype=np.int64)
-    params.alfa = np.array([[1,1,1,1,0,1,0,0,2],
-                            [0,0,0,1,0,0,0,0,1],
-                            [0,0,1,0,2,1,1,1,0],
-                            [0,0,0,0,0,0,0,0,0],
-                            [0,0,0,0,0,0,0,0,0],
-                            [1,1,0,0,0,0,1,0,0]], dtype=np.int64)
-	# Rxn1:  E + AR   ->  2E  +  AR+
-	# Rxn2:  E + AR   ->  E   +  AR*
-	# Rxn3:  E + AR*  ->  2E  +  AR+
-	# Rxn4:  E + AR+  ->  AR*
-	# Rxn5:  2AR*     ->  E   +  AR+  +  AR
-	# Rxn6:  E + AR*  ->  E   +  AR
-	# Rxn7:  AR* + AR ->  2AR
-	# Rxn8:  AR*      ->  AR
-	# Rxn9:  2E + AR+ ->  E   +  AR*
-
-    # 4) Set values in params class
-    params.D[0]    = De
-    params.D[1]    = Di
-    params.D[2]    = Dm
-    params.D[3]    = Dr
-    params.D[4]    = D4p
-
-    params.mu[0]   = mue
-    params.mu[1]   = mui
-    params.mu[2]   = mum
-    params.mu[3]   = mur
-    params.mu[4]   = mu4p
-
-    params.A[:]    = Ck[:]
-    params.B[:]    = B[:]
-    params.C[:]    = A[:]
-
-    # Account for the 2/3 term to convert from electron temperature to electron energy
-    for i in range(len(params.A)):
-        params.A[i] *= (2/3)**(params.B[i])
-
-    params.dH[:]   = dH[:]
-    params.dEps[:] = dEps[:]
-    params.qStar   = qStar
-    params.alpha   = alpha
-    params.ks      = ks
-    params.gam     = gam
-    params.kappaB  = kappaB
-    params.nAronp0 = nAr / np0
-    params.p0      = p0
-    params.Tg0     = Tg0
-    params.EC      = 2.0 * me / mAr \
-        * np.sqrt(16.0 * (me + mAr) * e0 * c**2
-                  / (3.0 * np.pi * me * mAr)) * se * nAr * tau
-    # params.EC = 2.0 * me / mAr * 3.8e9 * tau
-
-    params.verticalShift = verticalShift / V0
-
-    # Parameters needed to compute the current with dimensions
-    params.V0Ltau  = V0 / (L * tau)
-    params.V0L     = V0 / L
-    params.LLV0tau = (L*L) / (V0*tau)
-    params.tauL    = L / tau
-    params.np0     = np0           # "nominal" electron density [1/m^3]
-    params.qe      = qe            # unit charge [C]
-    params.eps0    = eps0          # unit charge [C]
-    params.eArea   = electrodeArea # electrode area [m^2]
-
-
-    reactionExpressionslist = [f"{params.A[0]} * energy**{params.B[0]} * np.exp(-{params.C[0]} / energy)",
-                                f"{params.A[1]} * energy**{params.B[1]} * np.exp(-{params.C[1]} / energy)",
-                                f"{params.A[2]} * energy**{params.B[2]} * np.exp(-{params.C[2]} / energy)",
-                                f"{params.A[3]} * energy**{params.B[3]} * np.exp(-{params.C[3]} / energy)",
-                                f"{params.A[4]} * energy**{params.B[4]} * np.exp(-{params.C[4]} / energy)",
-                                f"{params.A[5]} * energy**{params.B[5]} * np.exp(-{params.C[5]} / energy)",
-                                f"{params.A[6]} * energy**{params.B[6]} * np.exp(-{params.C[6]} / energy)",
-				f"{params.A[7]} * energy**{params.B[7]} * np.exp(-{params.C[7]} / energy)",
-				f"{params.A[8]} * energy**{params.B[8]} * np.exp(-{params.C[8]} / energy)"]
-
-    reactionTExpressionslist = [f"{params.A[0]} * (energy**({params.B[0]}-1)) * np.exp(-{params.C[0]}/energy) * ({params.B[0]} + {params.C[0]}/energy)",
-                                f"{params.A[1]} * (energy**({params.B[1]}-1)) * np.exp(-{params.C[1]}/energy) * ({params.B[1]} + {params.C[1]}/energy)",
-                                f"{params.A[2]} * (energy**({params.B[2]}-1)) * np.exp(-{params.C[2]}/energy) * ({params.B[2]} + {params.C[2]}/energy)",
-                                f"{params.A[3]} * (energy**({params.B[3]}-1)) * np.exp(-{params.C[3]}/energy) * ({params.B[3]} + {params.C[3]}/energy)",
-                                f"{params.A[4]} * (energy**({params.B[4]}-1)) * np.exp(-{params.C[4]}/energy) * ({params.B[4]} + {params.C[4]}/energy)",
-                                f"{params.A[5]} * (energy**({params.B[5]}-1)) * np.exp(-{params.C[5]}/energy) * ({params.B[5]} + {params.C[5]}/energy)",
-                                f"{params.A[6]} * (energy**({params.B[6]}-1)) * np.exp(-{params.C[6]}/energy) * ({params.B[6]} + {params.C[6]}/energy)",
-				f"{params.A[7]} * (energy**({params.B[7]}-1)) * np.exp(-{params.C[7]}/energy) * ({params.B[7]} + {params.C[7]}/energy)",
-				f"{params.A[8]} * (energy**({params.B[8]}-1)) * np.exp(-{params.C[8]}/energy) * ({params.B[8]} + {params.C[8]}/energy)"]
-
-    reactionExpressionTypelist =  np.array([True,True,True,False,False,False,False,False,False])
-
-    reactionsList = []
-    LOGFilename = 'interpolationSample%s.log'%str(iSample)
-    f = open(LOGFilename, 'w')
-	# import h5py as h5
-	# rxnName = ["Ionization", ...] <- dictionary containing reaction name root string
-	# for r in range(Nr):
-	#	if reactionExpressionTypeList[r]:
-	#		fileName = "{0:s}.{1:08d}.h5".format(rxnName[r], iSample)
-	#		f = h5.File(filename, "r")
-	#		D = f["table"]
-
-    for i in range(Nr):
-        if reactionExpressionTypelist[i]:
-            Nsample = 1
-            N300 = 200
-
-            root_dir = ".."
-            rate_file = open("{0:s}/BOLSIGChemistry_NominalRates/reaction300K_{1:d}.txt".format(root_dir, i), 'r')
-            temp_file = open("{0:s}/BOLSIGChemistry_NominalRates/reaction300K_Te.txt".format(root_dir), 'r')
-            
-            #rateCoeff = np.fromfile(rate_file)
-            rateCoeff = np.genfromtxt(rate_file)
-            rate_file.close()
-            rateCoeff = np.reshape(rateCoeff,[Nsample, N300]).T[:,iSample]
-
-            #Te = np.fromfile(temp_file)
-            Te = np.genfromtxt(temp_file)
-            temp_file.close()
-            Te = np.reshape(Te,[Nsample, N300]).T[:,iSample]
-
-            # Sorting mean energy array and rate coefficient array based on
-            # the mean energy array.
-            Teinds = Te.argsort()
-            rateCoeff = rateCoeff[Teinds]
-            Te = Te[Teinds]
-
-            # Find duplicates
-            TeDuplicateinds = np.where(np.abs(np.diff(Te, axis=0)) > 0.0)
-            TeDuplicateindsForLog = np.where(np.abs(np.diff(Te, axis=0)) == 0.0)
-            rateCoeff = rateCoeff[TeDuplicateinds]
-            Te = Te[TeDuplicateinds]
-
-            # Nondimensionalization of mean energy.
-            Te *= 1.5
-
-            # Find first non-zero value of the coefficient rate.
-            I = np.nonzero(rateCoeff)
-
-            diffRateCoeff = [j-i for i, j in zip(rateCoeff[:-1], rateCoeff[1:])]
-            diffTe = [j-i for i, j in zip(Te[:-1], Te[1:])]
-
-            Monotonicity = np.asarray([j/i for i, j in zip(diffTe, diffRateCoeff)])
-            Monotonicity = np.insert(Monotonicity, 0, 0.0, axis=0)
-
-            Nan = np.isnan(Monotonicity)
-            Inf = np.isinf(Monotonicity)
-            indexPositive = np.where(Monotonicity>0.0)
-            Positive = np.full(Monotonicity.shape, False, dtype=bool)
-            Positive[indexPositive] = True
-
-            indices = Nan + Inf + Positive
-
-            lastFalse = np.where(indices==False)[-1][-1] + 2
-
-            # Transformation to log scale.
-            TeLog = np.log(Te)
-
-            # Compute the slope of the rate coefficient between its first two non-zero values.
-            # Finite differences are used.
-            dydx = (rateCoeff[lastFalse + 1] - rateCoeff[lastFalse]) \
-                 / (Te[lastFalse + 1] - Te[lastFalse])
-
-            # Arrhenius form: kf = A * exp(-C / Te)
-            C = Te[lastFalse]**2.0*dydx / rateCoeff[lastFalse]
-
-            # Compute pre-exponential coefficient, A, in log scale.
-            ALog = np.log(rateCoeff[lastFalse]) + C / Te[lastFalse]
-
-            # Transform rate coefficient in log scale.
-            rateCoeffLog = np.zeros(rateCoeff.shape)
-            rateCoeffLog[lastFalse:] = np.log(rateCoeff[lastFalse:])
-            # For the troublesome values, we use the Arrhenius form.
-            rateCoeffLog[0:lastFalse] = ALog - C / Te[0:lastFalse]
-            # Nondimensionalization in log scale.
-            if i < 2:
-                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
-            else:
-                rateCoeffLog += - np.log(1.0/tau) + np.log(np0)
-
-            # Interpolation in log scale.
-            reactionExpressionsLog = CubicSpline(TeLog, rateCoeffLog)
-            # Gradient in log scale
-            reactionTExpressionsLog = CubicSpline.derivative(reactionExpressionsLog)
-
-            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
-                                rxnBolsig = reactionExpressionTypelist[i],
-                                kf_log = reactionExpressionsLog,
-                                kf_T_log = reactionTExpressionsLog)
-            reactionsList.append(reaction)
-
-            logging.basicConfig(filename=LOGFilename)
-            logging.warning('Interpolation info (Reaction %s):', i)
-            logging.warning('First non-zero entry in the rate coefficient: %s', I[0][0])
-            logging.warning('Monotonicity of the rate coefficient start from entry: %s', lastFalse)
-            logging.warning('Position of possible duplicates in mean energy array: %s',
-                          TeDuplicateindsForLog[0])
-
-        else:
-            rxn   = eval("lambda energy :" + reactionExpressionslist[i])
-            rxn_T = eval("lambda energy :" + reactionTExpressionslist[i])
-
-            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
-                                rxnBolsig = reactionExpressionTypelist[i],
-                                kf = rxn, kf_T = rxn_T)
-            reactionsList.append(reaction)
-
-    params.reactionsList = reactionsList
-
-    # 5) Dump to screen
-    params.print()
diff --git a/psaapProperties_6Species.py b/psaapProperties_6Species_100mTorr.py
similarity index 83%
rename from psaapProperties_6Species.py
rename to psaapProperties_6Species_100mTorr.py
index e02c158af..925628450 100755
--- a/psaapProperties_6Species.py
+++ b/psaapProperties_6Species_100mTorr.py
@@ -1,8 +1,9 @@
 import numpy as np
 from scipy.interpolate import CubicSpline
 import csv
-import matplotlib.pyplot as plt
-import matplotlib.colors as mcolors
+#import matplotlib.pyplot as plt
+#import matplotlib.colors as mcolors
+import h5py as h5
 
 import logging
 
@@ -14,7 +15,24 @@ def __init__(self, *initial_data, **kwargs):
         for key in kwargs:
             setattr(self, key, kwargs[key])
 
-def setPsaapProperties_6Species(gam, inputV0, inputVDC, params, Nr, iSample):
+class Diffusivity(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Mobility(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+
+def setPsaapProperties_6Species_100mTorr(gam, inputV0, inputVDC, params, Nr, iSample):
     """Sets non-dimensional properties corresponding to Liu 2014 paper.
 
     Inputs:
@@ -30,7 +48,7 @@ def setPsaapProperties_6Species(gam, inputV0, inputVDC, params, Nr, iSample):
     ###################################################################
 
     # densities
-    nAr = 3.22e22     # background number density of Ar [1/m^3] (corresponds to p=100 mTorr)
+    nAr = 3.22e21     # background number density of Ar [1/m^3] (corresponds to p=100 mTorr)
     np0 = 8e16        # "nominal" electron density [1/m^3]
 
     # masses
@@ -48,7 +66,7 @@ def setPsaapProperties_6Species(gam, inputV0, inputVDC, params, Nr, iSample):
     e0 = 1.0  # [eV]
 
     # pressure
-    p  = 133.3224*1.5      # [J/m^3] *1.5 to convert it to energy (1 Torr)
+    p  = 13.33*1.5      # [J/m^3] *1.5 to convert it to energy (1 Torr)
 
     # gas energy at the wall
     Tg0 = 0.038778    # 3/2*300K*kB ~ (p0 - nT[:,0])/ntot
@@ -62,20 +80,15 @@ def setPsaapProperties_6Species(gam, inputV0, inputVDC, params, Nr, iSample):
 
     # transport parameters
     nmue = 9.66e21   # argon number density times electron mobility [1/(V*cm*s)]
-    #nmui = 4.65e19   # argon number density times ion mobility [1/(V*cm*s)]
-    #nmum = 1 / (np.sqrt(16.0 * (mAr + mAr) * 300 * 8.62e-5 * c**2
-    #                    / (3.0 * np.pi * mAr * mAr)) * se * mAr * 1.6e-19 / c**2)
     nmum = 0.0
     nmur = 0.0
     nmu4p = 0.0
     nmui = 8.0e19
-    #nmum = 9.35e19
     nDe  = 3.86e22   # argon number density times electron diffusivity [1/(cm*s)]
     nDi  = 2.07e18   # argon number density times ion diffusivity [1/(cm*s)]
     nDm  = 2.42e18   # argon number density times AR(m) diffusivity [1/(cm*s)]
     nDr  = 2.42e18
     nD4p = 2.42e18
-    #nDm  = 3.914e20
 
     # reaction parameters (NB: k_i = Ck*Ee^B*exp(-A/Ee))
     #                          Ee = 3/2*Te (Te in eV)
@@ -115,7 +128,7 @@ def setPsaapProperties_6Species(gam, inputV0, inputVDC, params, Nr, iSample):
     nmui *= 100. # 1/(V*m*s)
     Ck[0:5] *= 1e-6 # m^3/s
     Ck[5:9] *= 1 # 1/s
-    Ck[9:22] *= 1e-6 # m^3/s
+    Ck[9:]  *= 1e-6 # m^3/s
     ks   *= 0.01 # m/s
     se   *= 1.0e-20  # m^2
 
@@ -197,6 +210,30 @@ def setPsaapProperties_6Species(gam, inputV0, inputVDC, params, Nr, iSample):
     # Rxn22: E + AR(r)      ->   E + AR(m)
     # Rxn23: E + AR(r)      ->   E + AR(4p)
 
+    rxnNameDict = { 0: "2Ar(m) => 2Ar",
+                    1: "Ar(m) + Ar(r) => E + Ar+ + Ar",
+                    2: "2Ar(4p) => E + Ar+ + Ar",
+                    3: "2Ar(m) => E + Ar+ + Ar",
+                    4: "Ar(m) + Ar => 2Ar",
+                    5: "Ar(r) => Ar",
+                    6: "Ar(4p) => Ar",
+                    7: "Ar(4p) => Ar(m)",
+                    8: "Ar(4p) => Ar(r)",
+                    9: "lumped.metastable",
+                   10: "lumped.resonance",
+                   11: "lumped.2p",
+                   12: "ionization",
+                   13: "E + Ar(m) => E + Ar",
+                   14: "step_ionization",
+                   15: "E + Ar(m) => E + Ar(r)",
+                   16: "E + Ar(m) => E + Ar(4p)",
+                   17: "E + Ar(4p) => 2E + Ar+",
+                   18: "E + Ar(4p) => E + Ar(r)",
+                   19: "E + Ar(4p) => E + Ar(m)",
+                   20: "E + Ar(r) => E + Ar",
+                   21: "E + Ar(r) => E + Ar(m)",
+                   22: "E + Ar(r) => E + Ar(4p)"}
+
     # 4) Set values in params class
     params.D[0]    = De
     params.D[1]    = Di
@@ -277,8 +314,8 @@ def setPsaapProperties_6Species(gam, inputV0, inputVDC, params, Nr, iSample):
                                 f"{params.A[4]} * (energy**({params.B[4]}-1)) * np.exp(-{params.C[4]}/energy) * ({params.B[4]} + {params.C[4]}/energy)",
                                 f"{params.A[5]} * (energy**({params.B[5]}-1)) * np.exp(-{params.C[5]}/energy) * ({params.B[5]} + {params.C[5]}/energy)",
                                 f"{params.A[6]} * (energy**({params.B[6]}-1)) * np.exp(-{params.C[6]}/energy) * ({params.B[6]} + {params.C[6]}/energy)",
-				                f"{params.A[7]} * (energy**({params.B[7]}-1)) * np.exp(-{params.C[7]}/energy) * ({params.B[7]} + {params.C[7]}/energy)",
-				                f"{params.A[8]} * (energy**({params.B[8]}-1)) * np.exp(-{params.C[8]}/energy) * ({params.B[8]} + {params.C[8]}/energy)",
+				f"{params.A[7]} * (energy**({params.B[7]}-1)) * np.exp(-{params.C[7]}/energy) * ({params.B[7]} + {params.C[7]}/energy)",
+		                f"{params.A[8]} * (energy**({params.B[8]}-1)) * np.exp(-{params.C[8]}/energy) * ({params.B[8]} + {params.C[8]}/energy)",
                                 f"{params.A[9]} * (energy**({params.B[9]}-1)) * np.exp(-{params.C[9]}/energy) * ({params.B[9]} + {params.C[9]}/energy)",
                                 f"{params.A[10]} * (energy**({params.B[10]}-1)) * np.exp(-{params.C[10]}/energy) * ({params.B[10]} + {params.C[10]}/energy)",
                                 f"{params.A[11]} * (energy**({params.B[11]}-1)) * np.exp(-{params.C[11]}/energy) * ({params.B[11]} + {params.C[11]}/energy)",
@@ -296,37 +333,25 @@ def setPsaapProperties_6Species(gam, inputV0, inputVDC, params, Nr, iSample):
            
 
     reactionExpressionTypelist =  np.array([False,False,False,False,False,False,False,False,False,
-                                           True,True,True,True,False,True,False,False,False,False,False,False,False,False])
+                                           True,True,True,True,False,True,True,True,False,True,True,False,True,True])
 
     reactionsList = []
-    LOGFilename = 'interpolationSample%s.log'%str(iSample)
+    LOGFilename = 'interpolationSample.log'
     f = open(LOGFilename, 'w')
-	# import h5py as h5
-	# rxnName = ["Ionization", ...] <- dictionary containing reaction name root string
-	# for r in range(Nr):
-	#	if reactionExpressionTypeList[r]:
-	#		fileName = "{0:s}.{1:08d}.h5".format(rxnName[r], iSample)
-	#		f = h5.File(filename, "r")
-	#		D = f["table"]
 
     for i in range(Nr):
         if reactionExpressionTypelist[i]:
-            Nsample = 1
-            N300 = 200
-
-            root_dir = ".."
-            rate_file = open("{0:s}/BOLSIGChemistry_6SpeciesRates/reaction300K_{1:d}.txt".format(root_dir, i), 'r')
-            temp_file = open("{0:s}/BOLSIGChemistry_6SpeciesRates/reaction300K_Te.txt".format(root_dir), 'r')
-            
-            #rateCoeff = np.fromfile(rate_file)
-            rateCoeff = np.genfromtxt(rate_file)
-            rate_file.close()
-            rateCoeff = np.reshape(rateCoeff,[Nsample, N300]).T[:,iSample]
+            if (i < 15):
+                f = h5.File("../BOLSIGChemistry_6SpeciesRates/{0:s}.h5".format(rxnNameDict[i]), 'r')
+                dataset = f["table"]
+            else:
+                f = h5.File("../BOLSIGChemistry_6SpeciesRates/StepwiseExcitations.nominal.h5", 'r')
+                dataset = f[rxnNameDict[i]]
 
-            #Te = np.fromfile(temp_file)
-            Te = np.genfromtxt(temp_file)
-            temp_file.close()
-            Te = np.reshape(Te,[Nsample, N300]).T[:,iSample]
+            Te = dataset[:,0]
+            Te /= 11604
+            rateCoeff = dataset[:,1]
+            rateCoeff /= 6.022e23
 
             # Sorting mean energy array and rate coefficient array based on
             # the mean energy array.
@@ -386,13 +411,7 @@ def setPsaapProperties_6Species(gam, inputV0, inputVDC, params, Nr, iSample):
             # For the troublesome values, we use the Arrhenius form.
             rateCoeffLog[0:lastFalse] = ALog - C / Te[0:lastFalse]
             # Nondimensionalization in log scale.
-            if i == 9:
-                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
-            elif i == 10:
-                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
-            elif i == 11:
-                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
-            elif i == 12:
+            if (i < 13):
                 rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
             else:
                 rateCoeffLog += - np.log(1.0/tau) + np.log(np0)
@@ -426,5 +445,39 @@ def setPsaapProperties_6Species(gam, inputV0, inputVDC, params, Nr, iSample):
 
     params.reactionsList = reactionsList
 
+    ## Electron Transport Data
+    diffList = []
+    transport = h5.File("../BOLSIGChemistry_Transport/nominal_transport.h5", 'r')
+    NDe_v_Te = transport["diffusivity"]
+    Te_trans = NDe_v_Te[:,0]
+    Te_trans /= 11604
+    print("Te_min = {0:.6e}".format(NDe_v_Te[0,0]))
+    print("Te_max = {0:.6e}".format(NDe_v_Te[-1,0]))
+    De_interp = (NDe_v_Te[:,1]/nAr)*tau/(L*L)
+    De_spline = CubicSpline(Te_trans, De_interp)
+    De_Te_spline = CubicSpline.derivative(De_spline)
+    diffusivity = Diffusivity(interpolate = True, D_expression = De_spline, D_T_expression = De_Te_spline)
+    diffList.append(diffusivity)
+
+    Ns = 6
+    for i in range(1, Ns):
+        diffList.append(Diffusivity(interpolate = False))
+
+    params.diffusivityList = diffList
+
+    muList = []
+    Nmue_v_Te = transport["mobility"]
+    mue_interp = (Nmue_v_Te[:,1]/nAr)*V0*tau/(L*L)
+    mue_spline = CubicSpline(Te_trans, mue_interp)
+    mue_Te_spline = CubicSpline.derivative(mue_spline)
+    mobility = Mobility(interpolate = True, mu_expression = mue_spline, mu_T_expression = mue_Te_spline)
+    muList.append(mobility)
+
+    Ns = 6
+    for i in range(1, Ns):
+        muList.append(Mobility(interpolate = False))
+
+    params.mobilityList = muList
+    
     # 5) Dump to screen
     params.print()
diff --git a/psaapProperties_6Species_100mTorr_Expanded.py b/psaapProperties_6Species_100mTorr_Expanded.py
new file mode 100755
index 000000000..237ec31f8
--- /dev/null
+++ b/psaapProperties_6Species_100mTorr_Expanded.py
@@ -0,0 +1,549 @@
+import numpy as np
+from scipy.interpolate import CubicSpline
+import csv
+#import matplotlib.pyplot as plt
+#import matplotlib.colors as mcolors
+import h5py as h5
+
+import logging
+
+class Reaction(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Diffusivity(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Mobility(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+
+def setPsaapProperties_6Species_100mTorr_Expanded(gam, inputV0, inputVDC, params, Nr, iSample):
+    """Sets non-dimensional properties corresponding to Liu 2014 paper.
+
+    Inputs:
+      gam : Secondary electron emission coefficient
+      params : chebSolver.modelParams class
+
+    Outputs: None
+      params data is overwritten using values from Liu 2014.
+    """
+    ###################################################################
+    # User specified parameters (you may change these if you wish to
+    # run a different scenario from Liu 2014)
+    ###################################################################
+
+    # densities
+    nAr = 3.22e21     # background number density of Ar [1/m^3] (corresponds to p = 100 mTorr)
+    np0 = 8e16        # "nominal" electron density [1/m^3]
+
+    # masses
+    # me = 9.10938356e-31        # mass of an electron [kg]
+    # me = 5.489e-4              # mass of an electron [u]
+    me = 0.511e6                 # mass of an electron [eV/c2]
+    # mAr = 39.948               # mass of an argon atom [u]
+    # mAr = 39.948 * 1.66054e-27 # mass of an argon atom [kg]
+    mAr = 37.2158e9              # mass of an argon atom [eV/c2]
+    # u = 931.4941e6             # eV/c2
+    c = 299792458                # speed of light [m/s]
+    se = 40                      # momentum cross section [A^2]
+
+    # nominal electron energy
+    e0 = 1.0  # [eV]
+
+    # pressure
+    p  = 13.3*1.5      # [J/m^3] *1.5 to convert it to energy (100 mTorr)
+
+    # gas energy at the wall
+    Tg0 = 0.038778    # 3/2*300K*kB ~ (p0 - nT[:,0])/ntot
+
+    # characteristics of driving voltage
+    V0  = inputV0                 # amplitude of driving voltage [V]
+    verticalShift = inputVDC      # DC voltage (vertical shift in driving voltage)
+    tau = (1./13.56e6)             # period of driving voltage [s]
+    L   = 2.00*0.005              # half-gap-width [m] (gap width is 2 cm)
+    electrodeArea = np.pi*0.05**2 # electrode area [m^2] (electrode diameter = 0.1 m)
+
+    # transport parameters
+    nmue = 9.66e21   # argon number density times electron mobility [1/(V*cm*s)]
+    nmum = 0.0
+    nmur = 0.0
+    nmu4p = 0.0
+    nmui = 8.0e19
+    nDe  = 3.86e22   # argon number density times electron diffusivity [1/(cm*s)]
+    nDi  = 2.07e18   # argon number density times ion diffusivity [1/(cm*s)]
+    nDm  = 2.42e18   # argon number density times AR(m) diffusivity [1/(cm*s)]
+    nDr  = 2.42e18
+    nD4p = 2.42e18
+
+    # reaction parameters (NB: k_i = Ck*Ee^B*exp(-A/Ee))
+    #                          Ee = 3/2*Te (Te in eV)
+    #                          -> k_i = [Ck*(2/3)^B] * Ee^B * exp[-(3/2)*A/Ee]
+    # nominal
+    Ck = np.array([2.0e-13,2.1e-15,5.0e-16,6.4e-16,2.1e-21,1.32e8,1.72e7,1.50e7,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0e-18,4.0e-19,0.0,0.0,0.0,0.0,2.5e-17,2.5e-17,1.0e-15,1.0e-15,0.0,0.0]) # pre-exponential factors [m^3/s]
+    B  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-0.5,0,0,0,0,0,0,0,0,0,0]) # Temperature Power
+    A  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]) # activation temperature [eV]
+    dH = np.array([0.0,-7.541,-10.577,-7.393,0.0,0.0,0.0,0.0,11.577,11.725,13.168,15.76,-11.577,4.183,0.148,1.592,2.592,-1.444,-1.592,-11.725,-0.148,1.444,0.0,0.0,-4.183,-4.035,-2.592,-15.76,0.0,0.0,-8.985,-9.133,4.035,-13.168]) # energy lost per electron due to ionization rxn [eV]
+    dEps = np.array([0.0,15.76,11.577,11.725,13.168,0.0]) # E, AR+, AR(m), AR(r), AR(4p), AR
+
+    # BC parameters
+    # ks = 1.19e7  # electron recombination rate [cm/s]
+    ks = 1.366109824889323e7 # electron recombination rate [cm/s/eV]
+
+    ###################################################################
+    # Constants of nature (probably shouldn't change unless you have
+    # root privileges on universe)
+    ###################################################################
+    qe   = 1.6e-19   # unit charge [C]
+    eps0 = 8.86e-12  # permittivity of free space [F/m]
+    kB   = 1.38e-23  # Boltzmann constant [J/K]
+    # kB   = 8.62e-5 # Boltzmann constant [eV/K]
+
+
+    ###################################################################
+    # Calculate non-dimensional parameters
+    ###################################################################
+
+    # 1) Convert input units to base SI (except eV)
+    nDe  *= 100. # 1/(m*s)
+    nDi  *= 100. # 1/(m*s)
+    nDm  *= 100.
+    nDr  *= 100.
+    nD4p *= 100.
+    nmue *= 100. # 1/(V*m*s)
+    nmui *= 100. # 1/(V*m*s)
+    ks   *= 0.01 # m/s
+    se   *= 1.0e-20  # m^2
+
+    # 2) Compute "raw" transport parameters
+    De  = nDe/nAr
+    Di  = nDi/nAr
+    Dm  = nDm/nAr
+    Dr  = nDr/nAr
+    D4p = nD4p/nAr
+
+    mue = nmue/nAr
+    mui = nmui/nAr
+    mum = nmum/nAr
+    mur = nmur/nAr
+    mu4p = nmu4p/nAr
+
+    # 3) Compute non-dimensional properties required by solver
+    De    = De*tau/(L*L)
+    Di    = Di*tau/(L*L)
+    Dm    = Dm*tau/(L*L)
+    Dr    = Dr*tau/(L*L)
+    D4p   = D4p*tau/(L*L)
+
+    mue   = mue*V0*tau/(L*L)
+    mui   = mui*V0*tau/(L*L)
+    mum   = mum*V0*tau/(L*L)
+    mur   = mur*V0*tau/(L*L)
+    mu4p  = mu4p*V0*tau/(L*L)
+
+    Ck[0:4]  *= tau*np0
+    Ck[4]    *= tau*nAr
+    Ck[5:8]  *= tau
+    Ck[8:12] *= tau*nAr
+    Ck[12:24]  *= tau*np0
+    Ck[24:28] *= tau*np0*np0
+    Ck[28:30] *= tau*nAr
+    Ck[30:] *= tau*np0
+    A        = A*1.5/e0  # 1.5 to convert from temperature to energy
+    dH       = dH/e0
+    qStar    = V0/e0 # qe*V0/e0, since e0 in eV, need qe*V0 in eV, which is just V0 in V
+    alpha    = qe*np0*L*L/(V0*eps0)
+    ks       = ks*tau/L
+    p0       = p/qe/np0
+    kappaB   = 4.878171165833662*1.6129 # non-dimensional thermal conductivity of background specie
+                                # (2/3)*tau/L**2*Kb/np0/kB,
+                                # where Kb is the thermal conductivity of background specie
+
+    params.beta = np.array([[0,1,1,1,0,0,0,0,1,1,1,2,1,2,1,1,2,1,1,1,1,1,0,0,1,1,1,1,0,0,1,1,2,1],                     # E
+                            [0,1,1,1,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0],                     # AR+
+                            [0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,1,0,0,0,0,0],                     # AR(m)
+                            [0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0],                     # AR(r)
+                            [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0],                     # AR(4p)
+                            [2,1,1,1,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1]], dtype=np.int64)    # AR
+
+    params.alfa = np.array([[0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,0,0,0,0,1,1],                     # E
+                            [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0],                     # AR+
+                            [2,1,0,2,1,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],                     # AR(m)
+                            [0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,1,0],                     # AR(r)
+                            [0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,0,1],                     # AR(4p)
+                            [0,0,0,0,1,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0]], dtype=np.int64)    # AR
+    
+    # Rxn1:  2AR(m)         ->   2AR
+    # Rxn2:  AR(m) + AR(r)  ->   E + AR+ + AR
+    # Rxn3:  2AR(4p)        ->   E + AR+ + AR
+    # Rxn4:  2AR(m)         ->   E + AR+ + AR
+    # Rxn5:  AR(m) + AR     ->   2AR
+    # Rxn6:  AR(r)          ->   AR + hv
+    # Rxn7:  AR(4p)         ->   AR(m) + hv
+    # Rxn8:  AR(4p)         ->   AR(r) + hv
+    # Rxn9:  E + AR         ->   E + AR(m)
+    # Rxn10: E + AR         ->   E + AR(r)
+    # Rxn11: E + AR         ->   E + AR(4p)
+    # Rxn12: E + AR         ->   2E + AR+
+    # Rxn13: E + AR(m)      ->   E + AR
+    # Rxn14: E + AR(m)      ->   2E + AR+
+    # Rxn15: E + AR(m)      ->   E + AR(r)
+    # Rxn16: E + AR(m)      ->   E + AR(4p)
+    # Rxn17: E + AR(4p)     ->   2E + AR+
+    # Rxn18: E + AR(4p)     ->   E + AR(r)
+    # Rxn19: E + AR(4p)     ->   E + AR(m)
+    # Rxn20: E + AR(r)      ->   E + AR
+    # Rxn21: E + AR(r)      ->   E + AR(m)
+    # Rxn22: E + AR(r)      ->   E + AR(4p)
+    # Rxn23: E + AR+        ->   AR(m) + hv
+    # Rxn24: E + AR+        ->   AR(4p) + hv
+    # Rxn25: 2E + AR+       ->   E + AR(m)
+    # Rxn26: 2E + AR+       ->   E + AR(r)
+    # Rxn27: 2E + AR+       ->   E + AR(4p)
+    # Rxn28: 2E + AR+       ->   E + AR
+    # Rxn29: AR + AR(4p)    ->   AR + AR(m)
+    # Rxn30: AR + AR(4p)    ->   AR + AR(r)
+    # Rxn31: AR(m) + AR(4p) ->   E + AR+ + AR
+    # Rxn32: AR(r) + AR(4p) ->   E + AR+ + AR
+    # Rxn33: E + AR(r)      ->   2E + AR+
+    # Rxn34: E + AR(4p)     ->   E + AR
+
+    rxnNameDict = { 0: "2Ar(m) => 2Ar",
+                    1: "Ar(m) + Ar(r) => E + Ar+ + Ar",
+                    2: "2Ar(4p) => E + Ar+ + Ar",
+                    3: "2Ar(m) => E + Ar+ + Ar",
+                    4: "Ar(m) + Ar => 2Ar",
+                    5: "Ar(r) => Ar",
+                    6: "Ar(4p) => Ar(m)",
+                    7: "Ar(4p) => Ar(r)",
+                    8: "Excitation_Metastable",
+                    9: "Excitation_Resonant",
+                   10: "Excitation_4p",
+                   11: "Ionization",
+                   12: "DeExcitation_Metastable",
+                   13: "StepIonization_Metastable",
+                   14: "E + Ar(m) => E + Ar(r)",
+                   15: "E + Ar(m) => E + Ar(4p)",
+                   16: "StepIonization_4p",
+                   17: "E + Ar(4p) => E + Ar(r)",
+                   18: "E + Ar(4p) => E + Ar(m)",
+                   19: "DeExcitation_Resonant",
+                   20: "E + Ar(r) => E + Ar(m)",
+                   21: "E + Ar(r) => E + Ar(4p)",
+                   22: "E + Ar+ => Ar(m)",
+                   23: "E + Ar+ => Ar(4p)",
+                   24: "3BdyRecomb_Metastable",
+                   25: "3BdyRecomb_Resonant",
+                   26: "3BdyRecomb_4p",
+                   27: "3BdyRecomb_Ground",
+                   28: "Ar + Ar(4p) => Ar + Ar(m)",
+                   29: "Ar + Ar(4p) => Ar + Ar(r)",
+                   30: "Ar(m) + Ar(4p) => E + Ar+ + Ar",
+                   31: "Ar(r) + Ar(4p) => E + Ar+ + Ar",
+                   32: "StepIonization_Resonant",
+                   33: "DeExcitation_4p"}
+
+    # 4) Set values in params class
+    params.D[0]    = De
+    params.D[1]    = Di
+    params.D[2]    = Dm
+    params.D[3]    = Dr
+    params.D[4]    = D4p
+
+    params.mu[0]   = mue
+    params.mu[1]   = mui
+    params.mu[2]   = mum
+    params.mu[3]   = mur
+    params.mu[4]   = mu4p
+
+    params.A[:]    = Ck[:]
+    params.B[:]    = B[:]
+    params.C[:]    = A[:]
+
+    # Account for the 2/3 term to convert from electron temperature to electron energy
+    for i in range(len(params.A)):
+        params.A[i] *= (2/3)**(params.B[i])
+
+    params.dH[:]   = dH[:]
+    params.dEps[:] = dEps[:]
+    params.qStar   = qStar
+    params.alpha   = alpha
+    params.ks      = ks
+    params.gam     = gam
+    params.kappaB  = kappaB
+    params.nAronp0 = nAr / np0
+    params.p0      = p0
+    params.Tg0     = Tg0
+    params.EC      = 2.0 * me / mAr \
+        * np.sqrt(16.0 * (me + mAr) * e0 * c**2
+                  / (3.0 * np.pi * me * mAr)) * se * nAr * tau
+    # params.EC = 2.0 * me / mAr * 3.8e9 * tau
+
+    params.verticalShift = verticalShift / V0
+
+    # Parameters needed to compute the current with dimensions
+    params.V0Ltau  = V0 / (L * tau)
+    params.V0L     = V0 / L
+    params.LLV0tau = (L*L) / (V0*tau)
+    params.tauL    = L / tau
+    params.np0     = np0           # "nominal" electron density [1/m^3]
+    params.qe      = qe            # unit charge [C]
+    params.eps0    = eps0          # unit charge [C]
+    params.eArea   = electrodeArea # electrode area [m^2]
+
+
+    reactionExpressionslist = [f"{params.A[0]} * energy**{params.B[0]} * np.exp(-{params.C[0]} / energy)",
+                               f"{params.A[1]} * energy**{params.B[1]} * np.exp(-{params.C[1]} / energy)",
+                               f"{params.A[2]} * energy**{params.B[2]} * np.exp(-{params.C[2]} / energy)",
+                               f"{params.A[3]} * energy**{params.B[3]} * np.exp(-{params.C[3]} / energy)",
+                               f"{params.A[4]} * energy**{params.B[4]} * np.exp(-{params.C[4]} / energy)",
+                               f"{params.A[5]} * energy**{params.B[5]} * np.exp(-{params.C[5]} / energy)",
+                               f"{params.A[6]} * energy**{params.B[6]} * np.exp(-{params.C[6]} / energy)",
+                               f"{params.A[7]} * energy**{params.B[7]} * np.exp(-{params.C[7]} / energy)",
+                               f"{params.A[8]} * energy**{params.B[8]} * np.exp(-{params.C[8]} / energy)",
+                               f"{params.A[9]} * energy**{params.B[9]} * np.exp(-{params.C[9]} / energy)",
+                               f"{params.A[10]} * energy**{params.B[10]} * np.exp(-{params.C[10]} / energy)",
+                               f"{params.A[11]} * energy**{params.B[11]} * np.exp(-{params.C[11]} / energy)",
+                               f"{params.A[12]} * energy**{params.B[12]} * np.exp(-{params.C[12]} / energy)",
+                               f"{params.A[13]} * energy**{params.B[13]} * np.exp(-{params.C[13]} / energy)",
+                               f"{params.A[14]} * energy**{params.B[14]} * np.exp(-{params.C[14]} / energy)",
+                               f"{params.A[15]} * energy**{params.B[15]} * np.exp(-{params.C[15]} / energy)",
+                               f"{params.A[16]} * energy**{params.B[16]} * np.exp(-{params.C[16]} / energy)",
+                               f"{params.A[17]} * energy**{params.B[17]} * np.exp(-{params.C[17]} / energy)",
+                               f"{params.A[18]} * energy**{params.B[18]} * np.exp(-{params.C[18]} / energy)",
+                               f"{params.A[19]} * energy**{params.B[19]} * np.exp(-{params.C[19]} / energy)",
+                               f"{params.A[20]} * energy**{params.B[20]} * np.exp(-{params.C[20]} / energy)",
+                               f"{params.A[21]} * energy**{params.B[21]} * np.exp(-{params.C[21]} / energy)",
+                               f"{params.A[22]} * energy**{params.B[22]} * np.exp(-{params.C[22]} / energy)",
+                               f"{params.A[23]} * energy**{params.B[23]} * np.exp(-{params.C[23]} / energy)",
+                               f"{params.A[24]} * energy**{params.B[24]} * np.exp(-{params.C[24]} / energy)",
+                               f"{params.A[25]} * energy**{params.B[25]} * np.exp(-{params.C[25]} / energy)",
+                               f"{params.A[26]} * energy**{params.B[26]} * np.exp(-{params.C[26]} / energy)",
+                               f"{params.A[27]} * energy**{params.B[27]} * np.exp(-{params.C[27]} / energy)",
+                               f"{params.A[28]} * energy**{params.B[28]} * np.exp(-{params.C[28]} / energy)",
+                               f"{params.A[29]} * energy**{params.B[29]} * np.exp(-{params.C[29]} / energy)",
+                               f"{params.A[30]} * energy**{params.B[30]} * np.exp(-{params.C[30]} / energy)",
+                               f"{params.A[31]} * energy**{params.B[31]} * np.exp(-{params.C[31]} / energy)",
+                               f"{params.A[32]} * energy**{params.B[32]} * np.exp(-{params.C[32]} / energy)",
+                               f"{params.A[33]} * energy**{params.B[33]} * np.exp(-{params.C[33]} / energy)"]
+
+
+    reactionTExpressionslist = [f"{params.A[0]} * (energy**({params.B[0]}-1)) * np.exp(-{params.C[0]}/energy) * ({params.B[0]} + {params.C[0]}/energy)",
+                                f"{params.A[1]} * (energy**({params.B[1]}-1)) * np.exp(-{params.C[1]}/energy) * ({params.B[1]} + {params.C[1]}/energy)",
+                                f"{params.A[2]} * (energy**({params.B[2]}-1)) * np.exp(-{params.C[2]}/energy) * ({params.B[2]} + {params.C[2]}/energy)",
+                                f"{params.A[3]} * (energy**({params.B[3]}-1)) * np.exp(-{params.C[3]}/energy) * ({params.B[3]} + {params.C[3]}/energy)",
+                                f"{params.A[4]} * (energy**({params.B[4]}-1)) * np.exp(-{params.C[4]}/energy) * ({params.B[4]} + {params.C[4]}/energy)",
+                                f"{params.A[5]} * (energy**({params.B[5]}-1)) * np.exp(-{params.C[5]}/energy) * ({params.B[5]} + {params.C[5]}/energy)",
+                                f"{params.A[6]} * (energy**({params.B[6]}-1)) * np.exp(-{params.C[6]}/energy) * ({params.B[6]} + {params.C[6]}/energy)",
+				f"{params.A[7]} * (energy**({params.B[7]}-1)) * np.exp(-{params.C[7]}/energy) * ({params.B[7]} + {params.C[7]}/energy)",
+		                f"{params.A[8]} * (energy**({params.B[8]}-1)) * np.exp(-{params.C[8]}/energy) * ({params.B[8]} + {params.C[8]}/energy)",
+                                f"{params.A[9]} * (energy**({params.B[9]}-1)) * np.exp(-{params.C[9]}/energy) * ({params.B[9]} + {params.C[9]}/energy)",
+                                f"{params.A[10]} * (energy**({params.B[10]}-1)) * np.exp(-{params.C[10]}/energy) * ({params.B[10]} + {params.C[10]}/energy)",
+                                f"{params.A[11]} * (energy**({params.B[11]}-1)) * np.exp(-{params.C[11]}/energy) * ({params.B[11]} + {params.C[11]}/energy)",
+                                f"{params.A[12]} * (energy**({params.B[12]}-1)) * np.exp(-{params.C[12]}/energy) * ({params.B[12]} + {params.C[12]}/energy)",
+                                f"{params.A[13]} * (energy**({params.B[13]}-1)) * np.exp(-{params.C[13]}/energy) * ({params.B[13]} + {params.C[13]}/energy)",
+                                f"{params.A[14]} * (energy**({params.B[14]}-1)) * np.exp(-{params.C[14]}/energy) * ({params.B[14]} + {params.C[14]}/energy)",
+                                f"{params.A[15]} * (energy**({params.B[15]}-1)) * np.exp(-{params.C[15]}/energy) * ({params.B[15]} + {params.C[15]}/energy)",
+                                f"{params.A[16]} * (energy**({params.B[16]}-1)) * np.exp(-{params.C[16]}/energy) * ({params.B[16]} + {params.C[16]}/energy)",
+                                f"{params.A[17]} * (energy**({params.B[17]}-1)) * np.exp(-{params.C[17]}/energy) * ({params.B[17]} + {params.C[17]}/energy)",
+                                f"{params.A[18]} * (energy**({params.B[18]}-1)) * np.exp(-{params.C[18]}/energy) * ({params.B[18]} + {params.C[18]}/energy)",
+                                f"{params.A[19]} * (energy**({params.B[19]}-1)) * np.exp(-{params.C[19]}/energy) * ({params.B[19]} + {params.C[19]}/energy)",
+                                f"{params.A[20]} * (energy**({params.B[20]}-1)) * np.exp(-{params.C[20]}/energy) * ({params.B[20]} + {params.C[20]}/energy)",
+                                f"{params.A[21]} * (energy**({params.B[21]}-1)) * np.exp(-{params.C[21]}/energy) * ({params.B[21]} + {params.C[21]}/energy)",
+                                f"{params.A[22]} * (energy**({params.B[22]}-1)) * np.exp(-{params.C[22]}/energy) * ({params.B[22]} + {params.C[22]}/energy)",
+                                f"{params.A[23]} * (energy**({params.B[23]}-1)) * np.exp(-{params.C[23]}/energy) * ({params.B[23]} + {params.C[23]}/energy)",
+                                f"{params.A[24]} * (energy**({params.B[24]}-1)) * np.exp(-{params.C[24]}/energy) * ({params.B[24]} + {params.C[24]}/energy)",
+                                f"{params.A[25]} * (energy**({params.B[25]}-1)) * np.exp(-{params.C[25]}/energy) * ({params.B[25]} + {params.C[25]}/energy)",
+                                f"{params.A[26]} * (energy**({params.B[26]}-1)) * np.exp(-{params.C[26]}/energy) * ({params.B[26]} + {params.C[26]}/energy)",
+                                f"{params.A[27]} * (energy**({params.B[27]}-1)) * np.exp(-{params.C[27]}/energy) * ({params.B[27]} + {params.C[27]}/energy)",
+                                f"{params.A[28]} * (energy**({params.B[28]}-1)) * np.exp(-{params.C[28]}/energy) * ({params.B[28]} + {params.C[28]}/energy)",
+                                f"{params.A[29]} * (energy**({params.B[29]}-1)) * np.exp(-{params.C[29]}/energy) * ({params.B[29]} + {params.C[29]}/energy)",
+                                f"{params.A[30]} * (energy**({params.B[30]}-1)) * np.exp(-{params.C[30]}/energy) * ({params.B[30]} + {params.C[30]}/energy)",
+                                f"{params.A[31]} * (energy**({params.B[31]}-1)) * np.exp(-{params.C[31]}/energy) * ({params.B[31]} + {params.C[31]}/energy)",
+                                f"{params.A[32]} * (energy**({params.B[32]}-1)) * np.exp(-{params.C[32]}/energy) * ({params.B[32]} + {params.C[32]}/energy)",
+                                f"{params.A[33]} * (energy**({params.B[33]}-1)) * np.exp(-{params.C[33]}/energy) * ({params.B[33]} + {params.C[33]}/energy)"]
+           
+
+    reactionExpressionTypelist =  np.array([False,False,False,False,False,False,False,False, # Rxns 1-8
+                                           True,True,True,True,True,True,True,True,True,True,True,True,True,True, # Rxns 9-21
+                                           False,False,True,True,True,True,False,False,False,False,True,True]) # Rxns 22-34
+
+    thresholded_rxn = np.array([False, False, False, False, False, False, False, False,
+                                True, True, True, True, False, True, True, True, True, False, False, False, False,
+                                True, False, False, False, False, False, False, False, False, False, False, True,
+                                False])
+    reactionsList = []
+    LOGFilename = 'interpolationSample.log'
+    f = open(LOGFilename, 'w')
+
+    for i in range(Nr):
+        if reactionExpressionTypelist[i]:
+            if i < 14 or i == 16 or i == 19 or i > 23:
+                f = h5.File("../../../BOLSIGChemistry_NominalRates/{0:s}.h5".format(rxnNameDict[i]), 'r')
+                dataset = f["table"]
+            else:
+                f = h5.File("../../../BOLSIGChemistry_NominalRates/StepExcitation.h5", 'r')
+                dataset = f[rxnNameDict[i]]
+
+            Te = dataset[:,0]
+            Te /= 11604
+            rateCoeff = dataset[:,1]
+            if i > 23 and i < 28:
+                rateCoeff /= 6.022e23**2
+            else:
+                rateCoeff /= 6.022e23
+
+            ## Removing BOLSIG failures
+            fail_inds = []
+            for j in range(len(rateCoeff)):
+                if rateCoeff[j] == 0.0 and j > np.nonzero(rateCoeff)[0][0]:
+                    fail_inds.append(j)
+
+            Te = np.delete(Te, fail_inds)
+            rateCoeff = np.delete(rateCoeff, fail_inds)
+
+            # Sorting mean energy array and rate coefficient array based on
+            # the mean energy array.
+            Teinds = Te.argsort()
+            rateCoeff = rateCoeff[Teinds]
+            Te = Te[Teinds]
+
+            # Find duplicates
+            TeDuplicateinds = np.where(np.abs(np.diff(Te, axis=0)) > 0.0)
+            TeDuplicateindsForLog = np.where(np.abs(np.diff(Te, axis=0)) == 0.0)
+            rateCoeff = rateCoeff[TeDuplicateinds]
+            Te = Te[TeDuplicateinds]
+
+            # Nondimensionalization of mean energy.
+            Te *= 1.5
+
+            # Find first non-zero value of the coefficient rate.
+            I = np.nonzero(rateCoeff)
+
+            diffRateCoeff = [j-i for i, j in zip(rateCoeff[:-1], rateCoeff[1:])]
+            diffTe = [j-i for i, j in zip(Te[:-1], Te[1:])]
+
+            Monotonicity = np.asarray([j/i for i, j in zip(diffTe, diffRateCoeff)])
+            Monotonicity = np.insert(Monotonicity, 0, 0.0, axis=0)
+
+            Nan = np.isnan(Monotonicity)
+            Inf = np.isinf(Monotonicity)
+            if thresholded_rxn[i] == True:
+                indexPositive = np.where(Monotonicity>0.0)
+            else:
+                indexPositive = np.where(Monotonicity<0.0)
+            Positive = np.full(Monotonicity.shape, False, dtype=bool)
+            Positive[indexPositive] = True
+            
+            indices = Nan + Inf + Positive
+
+
+            #lastFalse = np.where(indices==False)[-1][-1] + 2
+            for k in range(len(Te)):
+               if Te[k] < 4.5 and indices[k] == False:
+                  lastFalse = k + 2
+
+            # Transformation to log scale.
+            TeLog = np.log(Te)
+
+            # Compute the slope of the rate coefficient between its first two non-zero values.
+            # Finite differences are used.
+            dydx = (rateCoeff[lastFalse + 1] - rateCoeff[lastFalse]) \
+                 / (Te[lastFalse + 1] - Te[lastFalse])
+
+            # Arrhenius form: kf = A * exp(-C / Te)
+            C = Te[lastFalse]**2.0*dydx / rateCoeff[lastFalse]
+
+            # Compute pre-exponential coefficient, A, in log scale.
+            ALog = np.log(rateCoeff[lastFalse]) + C / Te[lastFalse]
+
+            # Transform rate coefficient in log scale.
+            rateCoeffLog = np.zeros(rateCoeff.shape)
+            rateCoeffLog[lastFalse:] = np.log(rateCoeff[lastFalse:])
+            # For the troublesome values, we use the Arrhenius form.
+            rateCoeffLog[0:lastFalse] = ALog - C / Te[0:lastFalse]
+            # Nondimensionalization in log scale.
+            if (i < 12):
+                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
+            elif i > 23 and i < 28:
+                rateCoeffLog += - np.log(1.0/tau) + 2*np.log(np0)
+            else:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(np0)
+
+            # Interpolation in log scale.
+            reactionExpressionsLog = CubicSpline(TeLog, rateCoeffLog)
+            # Gradient in log scale
+            reactionTExpressionsLog = CubicSpline.derivative(reactionExpressionsLog)
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf_log = reactionExpressionsLog,
+                                kf_T_log = reactionTExpressionsLog)
+            reactionsList.append(reaction)
+
+            logging.basicConfig(filename=LOGFilename)
+            logging.warning('Interpolation info (Reaction %s):', i + 1)
+            logging.warning('First non-zero entry in the rate coefficient: %s', I[0][0])
+            logging.warning('Monotonicity of the rate coefficient start from entry: %s', lastFalse)
+            logging.warning('Position of possible duplicates in mean energy array: %s',
+                          TeDuplicateindsForLog[0])
+
+        else:
+            rxn   = eval("lambda energy :" + reactionExpressionslist[i])
+            rxn_T = eval("lambda energy :" + reactionTExpressionslist[i])
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf = rxn, kf_T = rxn_T)
+            reactionsList.append(reaction)
+
+    params.reactionsList = reactionsList
+
+    ## Electron Transport Data
+    diffList = []
+    transport = h5.File("../../../BOLSIGChemistry_NominalRates/nominal_transport.h5", 'r')
+    NDe_v_Te = transport["diffusivity"]
+    Te_trans = NDe_v_Te[:,0]
+    Te_trans /= 11604
+    De_interp = (NDe_v_Te[:,1]/nAr)*tau/(L*L)
+    De_spline = CubicSpline(Te_trans, De_interp)
+    De_Te_spline = CubicSpline.derivative(De_spline)
+    diffusivity = Diffusivity(interpolate = True, D_expression = De_spline, D_T_expression = De_Te_spline)
+    diffList.append(diffusivity)
+
+    Ns = 6
+    for i in range(1, Ns):
+        diffList.append(Diffusivity(interpolate = False))
+
+    params.diffusivityList = diffList
+
+    muList = []
+    Nmue_v_Te = transport["mobility"]
+    mue_interp = (Nmue_v_Te[:,1]/nAr)*V0*tau/(L*L)
+    mue_spline = CubicSpline(Te_trans, mue_interp)
+    mue_Te_spline = CubicSpline.derivative(mue_spline)
+    mobility = Mobility(interpolate = True, mu_expression = mue_spline, mu_T_expression = mue_Te_spline)
+    muList.append(mobility)
+
+    Ns = 6
+    for i in range(1, Ns):
+        muList.append(Mobility(interpolate = False))
+
+    params.mobilityList = muList
+    
+    # 5) Dump to screen
+    params.print()
diff --git a/psaapProperties_6Species_10Torr_Expanded.py b/psaapProperties_6Species_10Torr_Expanded.py
new file mode 100755
index 000000000..9e6dc8be6
--- /dev/null
+++ b/psaapProperties_6Species_10Torr_Expanded.py
@@ -0,0 +1,547 @@
+import numpy as np
+from scipy.interpolate import CubicSpline
+import csv
+import h5py as h5
+
+import logging
+
+class Reaction(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Diffusivity(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Mobility(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+
+def setPsaapProperties_6Species_10Torr_Expanded(gam, inputV0, inputVDC, params, Nr, iSample):
+    """Sets non-dimensional properties corresponding to Liu 2014 paper.
+
+    Inputs:
+      gam : Secondary electron emission coefficient
+      params : chebSolver.modelParams class
+
+    Outputs: None
+      params data is overwritten using values from Liu 2014.
+    """
+    ###################################################################
+    # User specified parameters (you may change these if you wish to
+    # run a different scenario from Liu 2014)
+    ###################################################################
+
+    # densities
+    nAr = 3.22e23     # background number density of Ar [1/m^3] (corresponds to p = 10 Torr)
+    np0 = 8e16        # "nominal" electron density [1/m^3]
+
+    # masses
+    # me = 9.10938356e-31        # mass of an electron [kg]
+    # me = 5.489e-4              # mass of an electron [u]
+    me = 0.511e6                 # mass of an electron [eV/c2]
+    # mAr = 39.948               # mass of an argon atom [u]
+    # mAr = 39.948 * 1.66054e-27 # mass of an argon atom [kg]
+    mAr = 37.2158e9              # mass of an argon atom [eV/c2]
+    # u = 931.4941e6             # eV/c2
+    c = 299792458                # speed of light [m/s]
+    se = 40                      # momentum cross section [A^2]
+
+    # nominal electron energy
+    e0 = 1.0  # [eV]
+
+    # pressure
+    p  = 1333.3*1.5      # [J/m^3] *1.5 to convert it to energy (10 Torr)
+
+    # gas energy at the wall
+    Tg0 = 0.038778    # 3/2*300K*kB ~ (p0 - nT[:,0])/ntot
+
+    # characteristics of driving voltage
+    V0  = inputV0                 # amplitude of driving voltage [V]
+    verticalShift = inputVDC      # DC voltage (vertical shift in driving voltage)
+    tau = (1./13.56e6)             # period of driving voltage [s]
+    L   = 2.00*0.005              # half-gap-width [m] (gap width is 2 cm)
+    electrodeArea = np.pi*0.05**2 # electrode area [m^2] (electrode diameter = 0.1 m)
+
+    # transport parameters
+    nmue = 9.66e21   # argon number density times electron mobility [1/(V*cm*s)]
+    nmum = 0.0
+    nmur = 0.0
+    nmu4p = 0.0
+    nmui = 8.0e19
+    nDe  = 3.86e22   # argon number density times electron diffusivity [1/(cm*s)]
+    nDi  = 2.07e18   # argon number density times ion diffusivity [1/(cm*s)]
+    nDm  = 2.42e18   # argon number density times AR(m) diffusivity [1/(cm*s)]
+    nDr  = 2.42e18
+    nD4p = 2.42e18
+
+    # reaction parameters (NB: k_i = Ck*Ee^B*exp(-A/Ee))
+    #                          Ee = 3/2*Te (Te in eV)
+    #                          -> k_i = [Ck*(2/3)^B] * Ee^B * exp[-(3/2)*A/Ee]
+    # nominal
+    Ck = np.array([2.0e-13,2.1e-15,5.0e-16,6.4e-16,2.1e-21,1.32e8,1.72e7,1.50e7,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0e-18,4.0e-19,0.0,0.0,0.0,0.0,2.5e-17,2.5e-17,1.0e-15,1.0e-15,0.0,0.0]) # pre-exponential factors [m^3/s]
+    B  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-0.5,0,0,0,0,0,0,0,0,0,0]) # Temperature Power
+    A  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]) # activation temperature [eV]
+    dH = np.array([0.0,-7.541,-10.577,-7.393,0.0,0.0,0.0,0.0,11.577,11.725,13.168,15.76,-11.577,4.183,0.148,1.592,2.592,-1.444,-1.592,-11.725,-0.148,1.444,0.0,0.0,-4.183,-4.035,-2.592,-15.76,0.0,0.0,-8.985,-9.133,4.035,-13.168]) # energy lost per electron due to ionization rxn [eV]
+    dEps = np.array([0.0,15.76,11.577,11.725,13.168,0.0]) # E, AR+, AR(m), AR(r), AR(4p), AR
+
+    # BC parameters
+    # ks = 1.19e7  # electron recombination rate [cm/s]
+    ks = 1.366109824889323e7 # electron recombination rate [cm/s/eV]
+
+    ###################################################################
+    # Constants of nature (probably shouldn't change unless you have
+    # root privileges on universe)
+    ###################################################################
+    qe   = 1.6e-19   # unit charge [C]
+    eps0 = 8.86e-12  # permittivity of free space [F/m]
+    kB   = 1.38e-23  # Boltzmann constant [J/K]
+    # kB   = 8.62e-5 # Boltzmann constant [eV/K]
+
+
+    ###################################################################
+    # Calculate non-dimensional parameters
+    ###################################################################
+
+    # 1) Convert input units to base SI (except eV)
+    nDe  *= 100. # 1/(m*s)
+    nDi  *= 100. # 1/(m*s)
+    nDm  *= 100.
+    nDr  *= 100.
+    nD4p *= 100.
+    nmue *= 100. # 1/(V*m*s)
+    nmui *= 100. # 1/(V*m*s)
+    ks   *= 0.01 # m/s
+    se   *= 1.0e-20  # m^2
+
+    # 2) Compute "raw" transport parameters
+    De  = nDe/nAr
+    Di  = nDi/nAr
+    Dm  = nDm/nAr
+    Dr  = nDr/nAr
+    D4p = nD4p/nAr
+
+    mue = nmue/nAr
+    mui = nmui/nAr
+    mum = nmum/nAr
+    mur = nmur/nAr
+    mu4p = nmu4p/nAr
+
+    # 3) Compute non-dimensional properties required by solver
+    De    = De*tau/(L*L)
+    Di    = Di*tau/(L*L)
+    Dm    = Dm*tau/(L*L)
+    Dr    = Dr*tau/(L*L)
+    D4p   = D4p*tau/(L*L)
+
+    mue   = mue*V0*tau/(L*L)
+    mui   = mui*V0*tau/(L*L)
+    mum   = mum*V0*tau/(L*L)
+    mur   = mur*V0*tau/(L*L)
+    mu4p  = mu4p*V0*tau/(L*L)
+
+    Ck[0:4]  *= tau*np0
+    Ck[4]    *= tau*nAr
+    Ck[5:8]  *= tau
+    Ck[8:12] *= tau*nAr
+    Ck[12:24]  *= tau*np0
+    Ck[24:28] *= tau*np0*np0
+    Ck[28:30] *= tau*nAr
+    Ck[30:] *= tau*np0
+    A        = A*1.5/e0  # 1.5 to convert from temperature to energy
+    dH       = dH/e0
+    qStar    = V0/e0 # qe*V0/e0, since e0 in eV, need qe*V0 in eV, which is just V0 in V
+    alpha    = qe*np0*L*L/(V0*eps0)
+    ks       = ks*tau/L
+    p0       = p/qe/np0
+    kappaB   = 4.878171165833662*1.6129 # non-dimensional thermal conductivity of background specie
+                                # (2/3)*tau/L**2*Kb/np0/kB,
+                                # where Kb is the thermal conductivity of background specie
+
+    params.beta = np.array([[0,1,1,1,0,0,0,0,1,1,1,2,1,2,1,1,2,1,1,1,1,1,0,0,1,1,1,1,0,0,1,1,2,1],                     # E
+                            [0,1,1,1,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0],                     # AR+
+                            [0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,1,0,0,0,0,0],                     # AR(m)
+                            [0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0],                     # AR(r)
+                            [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0],                     # AR(4p)
+                            [2,1,1,1,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1]], dtype=np.int64)    # AR
+
+    params.alfa = np.array([[0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,0,0,0,0,1,1],                     # E
+                            [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0],                     # AR+
+                            [2,1,0,2,1,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],                     # AR(m)
+                            [0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,1,0],                     # AR(r)
+                            [0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,0,1],                     # AR(4p)
+                            [0,0,0,0,1,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0]], dtype=np.int64)    # AR
+    
+    # Rxn1:  2AR(m)         ->   2AR
+    # Rxn2:  AR(m) + AR(r)  ->   E + AR+ + AR
+    # Rxn3:  2AR(4p)        ->   E + AR+ + AR
+    # Rxn4:  2AR(m)         ->   E + AR+ + AR
+    # Rxn5:  AR(m) + AR     ->   2AR
+    # Rxn6:  AR(r)          ->   AR + hv
+    # Rxn7:  AR(4p)         ->   AR(m) + hv
+    # Rxn8:  AR(4p)         ->   AR(r) + hv
+    # Rxn9:  E + AR         ->   E + AR(m)
+    # Rxn10: E + AR         ->   E + AR(r)
+    # Rxn11: E + AR         ->   E + AR(4p)
+    # Rxn12: E + AR         ->   2E + AR+
+    # Rxn13: E + AR(m)      ->   E + AR
+    # Rxn14: E + AR(m)      ->   2E + AR+
+    # Rxn15: E + AR(m)      ->   E + AR(r)
+    # Rxn16: E + AR(m)      ->   E + AR(4p)
+    # Rxn17: E + AR(4p)     ->   2E + AR+
+    # Rxn18: E + AR(4p)     ->   E + AR(r)
+    # Rxn19: E + AR(4p)     ->   E + AR(m)
+    # Rxn20: E + AR(r)      ->   E + AR
+    # Rxn21: E + AR(r)      ->   E + AR(m)
+    # Rxn22: E + AR(r)      ->   E + AR(4p)
+    # Rxn23: E + AR+        ->   AR(m) + hv
+    # Rxn24: E + AR+        ->   AR(4p) + hv
+    # Rxn25: 2E + AR+       ->   E + AR(m)
+    # Rxn26: 2E + AR+       ->   E + AR(r)
+    # Rxn27: 2E + AR+       ->   E + AR(4p)
+    # Rxn28: 2E + AR+       ->   E + AR
+    # Rxn29: AR + AR(4p)    ->   AR + AR(m)
+    # Rxn30: AR + AR(4p)    ->   AR + AR(r)
+    # Rxn31: AR(m) + AR(4p) ->   E + AR+ + AR
+    # Rxn32: AR(r) + AR(4p) ->   E + AR+ + AR
+    # Rxn33: E + AR(r)      ->   2E + AR+
+    # Rxn34: E + AR(4p)     ->   E + AR
+
+    rxnNameDict = { 0: "2Ar(m) => 2Ar",
+                    1: "Ar(m) + Ar(r) => E + Ar+ + Ar",
+                    2: "2Ar(4p) => E + Ar+ + Ar",
+                    3: "2Ar(m) => E + Ar+ + Ar",
+                    4: "Ar(m) + Ar => 2Ar",
+                    5: "Ar(r) => Ar",
+                    6: "Ar(4p) => Ar(m)",
+                    7: "Ar(4p) => Ar(r)",
+                    8: "Excitation_Metastable",
+                    9: "Excitation_Resonant",
+                   10: "Excitation_4p",
+                   11: "Ionization",
+                   12: "DeExcitation_Metastable",
+                   13: "StepIonization_Metastable",
+                   14: "E + Ar(m) => E + Ar(r)",
+                   15: "E + Ar(m) => E + Ar(4p)",
+                   16: "StepIonization_4p",
+                   17: "E + Ar(4p) => E + Ar(r)",
+                   18: "E + Ar(4p) => E + Ar(m)",
+                   19: "DeExcitation_Resonant",
+                   20: "E + Ar(r) => E + Ar(m)",
+                   21: "E + Ar(r) => E + Ar(4p)",
+                   22: "E + Ar+ => Ar(m)",
+                   23: "E + Ar+ => Ar(4p)",
+                   24: "3BdyRecomb_Metastable",
+                   25: "3BdyRecomb_Resonant",
+                   26: "3BdyRecomb_4p",
+                   27: "3BdyRecomb_Ground",
+                   28: "Ar + Ar(4p) => Ar + Ar(m)",
+                   29: "Ar + Ar(4p) => Ar + Ar(r)",
+                   30: "Ar(m) + Ar(4p) => E + Ar+ + Ar",
+                   31: "Ar(r) + Ar(4p) => E + Ar+ + Ar",
+                   32: "StepIonization_Resonant",
+                   33: "DeExcitation_4p"}
+
+    # 4) Set values in params class
+    params.D[0]    = De
+    params.D[1]    = Di
+    params.D[2]    = Dm
+    params.D[3]    = Dr
+    params.D[4]    = D4p
+
+    params.mu[0]   = mue
+    params.mu[1]   = mui
+    params.mu[2]   = mum
+    params.mu[3]   = mur
+    params.mu[4]   = mu4p
+
+    params.A[:]    = Ck[:]
+    params.B[:]    = B[:]
+    params.C[:]    = A[:]
+
+    # Account for the 2/3 term to convert from electron temperature to electron energy
+    for i in range(len(params.A)):
+        params.A[i] *= (2/3)**(params.B[i])
+
+    params.dH[:]   = dH[:]
+    params.dEps[:] = dEps[:]
+    params.qStar   = qStar
+    params.alpha   = alpha
+    params.ks      = ks
+    params.gam     = gam
+    params.kappaB  = kappaB
+    params.nAronp0 = nAr / np0
+    params.p0      = p0
+    params.Tg0     = Tg0
+    params.EC      = 2.0 * me / mAr \
+        * np.sqrt(16.0 * (me + mAr) * e0 * c**2
+                  / (3.0 * np.pi * me * mAr)) * se * nAr * tau
+    # params.EC = 2.0 * me / mAr * 3.8e9 * tau
+
+    params.verticalShift = verticalShift / V0
+
+    # Parameters needed to compute the current with dimensions
+    params.V0Ltau  = V0 / (L * tau)
+    params.V0L     = V0 / L
+    params.LLV0tau = (L*L) / (V0*tau)
+    params.tauL    = L / tau
+    params.np0     = np0           # "nominal" electron density [1/m^3]
+    params.qe      = qe            # unit charge [C]
+    params.eps0    = eps0          # unit charge [C]
+    params.eArea   = electrodeArea # electrode area [m^2]
+
+
+    reactionExpressionslist = [f"{params.A[0]} * energy**{params.B[0]} * np.exp(-{params.C[0]} / energy)",
+                               f"{params.A[1]} * energy**{params.B[1]} * np.exp(-{params.C[1]} / energy)",
+                               f"{params.A[2]} * energy**{params.B[2]} * np.exp(-{params.C[2]} / energy)",
+                               f"{params.A[3]} * energy**{params.B[3]} * np.exp(-{params.C[3]} / energy)",
+                               f"{params.A[4]} * energy**{params.B[4]} * np.exp(-{params.C[4]} / energy)",
+                               f"{params.A[5]} * energy**{params.B[5]} * np.exp(-{params.C[5]} / energy)",
+                               f"{params.A[6]} * energy**{params.B[6]} * np.exp(-{params.C[6]} / energy)",
+                               f"{params.A[7]} * energy**{params.B[7]} * np.exp(-{params.C[7]} / energy)",
+                               f"{params.A[8]} * energy**{params.B[8]} * np.exp(-{params.C[8]} / energy)",
+                               f"{params.A[9]} * energy**{params.B[9]} * np.exp(-{params.C[9]} / energy)",
+                               f"{params.A[10]} * energy**{params.B[10]} * np.exp(-{params.C[10]} / energy)",
+                               f"{params.A[11]} * energy**{params.B[11]} * np.exp(-{params.C[11]} / energy)",
+                               f"{params.A[12]} * energy**{params.B[12]} * np.exp(-{params.C[12]} / energy)",
+                               f"{params.A[13]} * energy**{params.B[13]} * np.exp(-{params.C[13]} / energy)",
+                               f"{params.A[14]} * energy**{params.B[14]} * np.exp(-{params.C[14]} / energy)",
+                               f"{params.A[15]} * energy**{params.B[15]} * np.exp(-{params.C[15]} / energy)",
+                               f"{params.A[16]} * energy**{params.B[16]} * np.exp(-{params.C[16]} / energy)",
+                               f"{params.A[17]} * energy**{params.B[17]} * np.exp(-{params.C[17]} / energy)",
+                               f"{params.A[18]} * energy**{params.B[18]} * np.exp(-{params.C[18]} / energy)",
+                               f"{params.A[19]} * energy**{params.B[19]} * np.exp(-{params.C[19]} / energy)",
+                               f"{params.A[20]} * energy**{params.B[20]} * np.exp(-{params.C[20]} / energy)",
+                               f"{params.A[21]} * energy**{params.B[21]} * np.exp(-{params.C[21]} / energy)",
+                               f"{params.A[22]} * energy**{params.B[22]} * np.exp(-{params.C[22]} / energy)",
+                               f"{params.A[23]} * energy**{params.B[23]} * np.exp(-{params.C[23]} / energy)",
+                               f"{params.A[24]} * energy**{params.B[24]} * np.exp(-{params.C[24]} / energy)",
+                               f"{params.A[25]} * energy**{params.B[25]} * np.exp(-{params.C[25]} / energy)",
+                               f"{params.A[26]} * energy**{params.B[26]} * np.exp(-{params.C[26]} / energy)",
+                               f"{params.A[27]} * energy**{params.B[27]} * np.exp(-{params.C[27]} / energy)",
+                               f"{params.A[28]} * energy**{params.B[28]} * np.exp(-{params.C[28]} / energy)",
+                               f"{params.A[29]} * energy**{params.B[29]} * np.exp(-{params.C[29]} / energy)",
+                               f"{params.A[30]} * energy**{params.B[30]} * np.exp(-{params.C[30]} / energy)",
+                               f"{params.A[31]} * energy**{params.B[31]} * np.exp(-{params.C[31]} / energy)",
+                               f"{params.A[32]} * energy**{params.B[32]} * np.exp(-{params.C[32]} / energy)",
+                               f"{params.A[33]} * energy**{params.B[33]} * np.exp(-{params.C[33]} / energy)"]
+
+
+    reactionTExpressionslist = [f"{params.A[0]} * (energy**({params.B[0]}-1)) * np.exp(-{params.C[0]}/energy) * ({params.B[0]} + {params.C[0]}/energy)",
+                                f"{params.A[1]} * (energy**({params.B[1]}-1)) * np.exp(-{params.C[1]}/energy) * ({params.B[1]} + {params.C[1]}/energy)",
+                                f"{params.A[2]} * (energy**({params.B[2]}-1)) * np.exp(-{params.C[2]}/energy) * ({params.B[2]} + {params.C[2]}/energy)",
+                                f"{params.A[3]} * (energy**({params.B[3]}-1)) * np.exp(-{params.C[3]}/energy) * ({params.B[3]} + {params.C[3]}/energy)",
+                                f"{params.A[4]} * (energy**({params.B[4]}-1)) * np.exp(-{params.C[4]}/energy) * ({params.B[4]} + {params.C[4]}/energy)",
+                                f"{params.A[5]} * (energy**({params.B[5]}-1)) * np.exp(-{params.C[5]}/energy) * ({params.B[5]} + {params.C[5]}/energy)",
+                                f"{params.A[6]} * (energy**({params.B[6]}-1)) * np.exp(-{params.C[6]}/energy) * ({params.B[6]} + {params.C[6]}/energy)",
+				f"{params.A[7]} * (energy**({params.B[7]}-1)) * np.exp(-{params.C[7]}/energy) * ({params.B[7]} + {params.C[7]}/energy)",
+		                f"{params.A[8]} * (energy**({params.B[8]}-1)) * np.exp(-{params.C[8]}/energy) * ({params.B[8]} + {params.C[8]}/energy)",
+                                f"{params.A[9]} * (energy**({params.B[9]}-1)) * np.exp(-{params.C[9]}/energy) * ({params.B[9]} + {params.C[9]}/energy)",
+                                f"{params.A[10]} * (energy**({params.B[10]}-1)) * np.exp(-{params.C[10]}/energy) * ({params.B[10]} + {params.C[10]}/energy)",
+                                f"{params.A[11]} * (energy**({params.B[11]}-1)) * np.exp(-{params.C[11]}/energy) * ({params.B[11]} + {params.C[11]}/energy)",
+                                f"{params.A[12]} * (energy**({params.B[12]}-1)) * np.exp(-{params.C[12]}/energy) * ({params.B[12]} + {params.C[12]}/energy)",
+                                f"{params.A[13]} * (energy**({params.B[13]}-1)) * np.exp(-{params.C[13]}/energy) * ({params.B[13]} + {params.C[13]}/energy)",
+                                f"{params.A[14]} * (energy**({params.B[14]}-1)) * np.exp(-{params.C[14]}/energy) * ({params.B[14]} + {params.C[14]}/energy)",
+                                f"{params.A[15]} * (energy**({params.B[15]}-1)) * np.exp(-{params.C[15]}/energy) * ({params.B[15]} + {params.C[15]}/energy)",
+                                f"{params.A[16]} * (energy**({params.B[16]}-1)) * np.exp(-{params.C[16]}/energy) * ({params.B[16]} + {params.C[16]}/energy)",
+                                f"{params.A[17]} * (energy**({params.B[17]}-1)) * np.exp(-{params.C[17]}/energy) * ({params.B[17]} + {params.C[17]}/energy)",
+                                f"{params.A[18]} * (energy**({params.B[18]}-1)) * np.exp(-{params.C[18]}/energy) * ({params.B[18]} + {params.C[18]}/energy)",
+                                f"{params.A[19]} * (energy**({params.B[19]}-1)) * np.exp(-{params.C[19]}/energy) * ({params.B[19]} + {params.C[19]}/energy)",
+                                f"{params.A[20]} * (energy**({params.B[20]}-1)) * np.exp(-{params.C[20]}/energy) * ({params.B[20]} + {params.C[20]}/energy)",
+                                f"{params.A[21]} * (energy**({params.B[21]}-1)) * np.exp(-{params.C[21]}/energy) * ({params.B[21]} + {params.C[21]}/energy)",
+                                f"{params.A[22]} * (energy**({params.B[22]}-1)) * np.exp(-{params.C[22]}/energy) * ({params.B[22]} + {params.C[22]}/energy)",
+                                f"{params.A[23]} * (energy**({params.B[23]}-1)) * np.exp(-{params.C[23]}/energy) * ({params.B[23]} + {params.C[23]}/energy)",
+                                f"{params.A[24]} * (energy**({params.B[24]}-1)) * np.exp(-{params.C[24]}/energy) * ({params.B[24]} + {params.C[24]}/energy)",
+                                f"{params.A[25]} * (energy**({params.B[25]}-1)) * np.exp(-{params.C[25]}/energy) * ({params.B[25]} + {params.C[25]}/energy)",
+                                f"{params.A[26]} * (energy**({params.B[26]}-1)) * np.exp(-{params.C[26]}/energy) * ({params.B[26]} + {params.C[26]}/energy)",
+                                f"{params.A[27]} * (energy**({params.B[27]}-1)) * np.exp(-{params.C[27]}/energy) * ({params.B[27]} + {params.C[27]}/energy)",
+                                f"{params.A[28]} * (energy**({params.B[28]}-1)) * np.exp(-{params.C[28]}/energy) * ({params.B[28]} + {params.C[28]}/energy)",
+                                f"{params.A[29]} * (energy**({params.B[29]}-1)) * np.exp(-{params.C[29]}/energy) * ({params.B[29]} + {params.C[29]}/energy)",
+                                f"{params.A[30]} * (energy**({params.B[30]}-1)) * np.exp(-{params.C[30]}/energy) * ({params.B[30]} + {params.C[30]}/energy)",
+                                f"{params.A[31]} * (energy**({params.B[31]}-1)) * np.exp(-{params.C[31]}/energy) * ({params.B[31]} + {params.C[31]}/energy)",
+                                f"{params.A[32]} * (energy**({params.B[32]}-1)) * np.exp(-{params.C[32]}/energy) * ({params.B[32]} + {params.C[32]}/energy)",
+                                f"{params.A[33]} * (energy**({params.B[33]}-1)) * np.exp(-{params.C[33]}/energy) * ({params.B[33]} + {params.C[33]}/energy)"]
+           
+
+    reactionExpressionTypelist =  np.array([False,False,False,False,False,False,False,False, # Rxns 1-8
+                                           True,True,True,True,True,True,True,True,True,True,True,True,True,True, # Rxns 9-21
+                                           False,False,True,True,True,True,False,False,False,False,True,True]) # Rxns 22-34
+
+    thresholded_rxn = np.array([False, False, False, False, False, False, False, False,
+                                True, True, True, True, False, True, True, True, True, False, False, False, False,
+                                True, False, False, False, False, False, False, False, False, False, False, True,
+                                False])
+    reactionsList = []
+    LOGFilename = 'interpolationSample.log'
+    f = open(LOGFilename, 'w')
+
+    for i in range(Nr):
+        if reactionExpressionTypelist[i]:
+            if i < 14 or i == 16 or i == 19 or i > 23:
+                f = h5.File("../../../BOLSIGChemistry_NominalRates/{0:s}.h5".format(rxnNameDict[i]), 'r')
+                dataset = f["table"]
+            else:
+                f = h5.File("../../../BOLSIGChemistry_NominalRates/StepExcitation.h5", 'r')
+                dataset = f[rxnNameDict[i]]
+
+            Te = dataset[:,0]
+            Te /= 11604
+            rateCoeff = dataset[:,1]
+            if i > 23 and i < 28:
+                rateCoeff /= 6.022e23**2
+            else:
+                rateCoeff /= 6.022e23
+
+            ## Removing BOLSIG failures
+            fail_inds = []
+            for j in range(len(rateCoeff)):
+                if rateCoeff[j] == 0.0 and j > np.nonzero(rateCoeff)[0][0]:
+                    fail_inds.append(j)
+
+            Te = np.delete(Te, fail_inds)
+            rateCoeff = np.delete(rateCoeff, fail_inds)
+
+            # Sorting mean energy array and rate coefficient array based on
+            # the mean energy array.
+            Teinds = Te.argsort()
+            rateCoeff = rateCoeff[Teinds]
+            Te = Te[Teinds]
+
+            # Find duplicates
+            TeDuplicateinds = np.where(np.abs(np.diff(Te, axis=0)) > 0.0)
+            TeDuplicateindsForLog = np.where(np.abs(np.diff(Te, axis=0)) == 0.0)
+            rateCoeff = rateCoeff[TeDuplicateinds]
+            Te = Te[TeDuplicateinds]
+
+            # Nondimensionalization of mean energy.
+            Te *= 1.5
+
+            # Find first non-zero value of the coefficient rate.
+            I = np.nonzero(rateCoeff)
+
+            diffRateCoeff = [j-i for i, j in zip(rateCoeff[:-1], rateCoeff[1:])]
+            diffTe = [j-i for i, j in zip(Te[:-1], Te[1:])]
+
+            Monotonicity = np.asarray([j/i for i, j in zip(diffTe, diffRateCoeff)])
+            Monotonicity = np.insert(Monotonicity, 0, 0.0, axis=0)
+
+            Nan = np.isnan(Monotonicity)
+            Inf = np.isinf(Monotonicity)
+            if thresholded_rxn[i] == True:
+                indexPositive = np.where(Monotonicity>0.0)
+            else:
+                indexPositive = np.where(Monotonicity<0.0)
+            Positive = np.full(Monotonicity.shape, False, dtype=bool)
+            Positive[indexPositive] = True
+            
+            indices = Nan + Inf + Positive
+
+
+            #lastFalse = np.where(indices==False)[-1][-1] + 2
+            for k in range(len(Te)):
+               if Te[k] < 4.5 and indices[k] == False:
+                  lastFalse = k + 2
+
+            # Transformation to log scale.
+            TeLog = np.log(Te)
+
+            # Compute the slope of the rate coefficient between its first two non-zero values.
+            # Finite differences are used.
+            dydx = (rateCoeff[lastFalse + 1] - rateCoeff[lastFalse]) \
+                 / (Te[lastFalse + 1] - Te[lastFalse])
+
+            # Arrhenius form: kf = A * exp(-C / Te)
+            C = Te[lastFalse]**2.0*dydx / rateCoeff[lastFalse]
+
+            # Compute pre-exponential coefficient, A, in log scale.
+            ALog = np.log(rateCoeff[lastFalse]) + C / Te[lastFalse]
+
+            # Transform rate coefficient in log scale.
+            rateCoeffLog = np.zeros(rateCoeff.shape)
+            rateCoeffLog[lastFalse:] = np.log(rateCoeff[lastFalse:])
+            # For the troublesome values, we use the Arrhenius form.
+            rateCoeffLog[0:lastFalse] = ALog - C / Te[0:lastFalse]
+            # Nondimensionalization in log scale.
+            if (i < 12):
+                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
+            elif i > 23 and i < 28:
+                rateCoeffLog += - np.log(1.0/tau) + 2*np.log(np0)
+            else:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(np0)
+
+            # Interpolation in log scale.
+            reactionExpressionsLog = CubicSpline(TeLog, rateCoeffLog)
+            # Gradient in log scale
+            reactionTExpressionsLog = CubicSpline.derivative(reactionExpressionsLog)
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf_log = reactionExpressionsLog,
+                                kf_T_log = reactionTExpressionsLog)
+            reactionsList.append(reaction)
+
+            logging.basicConfig(filename=LOGFilename)
+            logging.warning('Interpolation info (Reaction %s):', i + 1)
+            logging.warning('First non-zero entry in the rate coefficient: %s', I[0][0])
+            logging.warning('Monotonicity of the rate coefficient start from entry: %s', lastFalse)
+            logging.warning('Position of possible duplicates in mean energy array: %s',
+                          TeDuplicateindsForLog[0])
+
+        else:
+            rxn   = eval("lambda energy :" + reactionExpressionslist[i])
+            rxn_T = eval("lambda energy :" + reactionTExpressionslist[i])
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf = rxn, kf_T = rxn_T)
+            reactionsList.append(reaction)
+
+    params.reactionsList = reactionsList
+
+    ## Electron Transport Data
+    diffList = []
+    transport = h5.File("../../../BOLSIGChemistry_NominalRates/nominal_transport.h5", 'r')
+    NDe_v_Te = transport["diffusivity"]
+    Te_trans = NDe_v_Te[:,0]
+    Te_trans /= 11604
+    De_interp = (NDe_v_Te[:,1]/nAr)*tau/(L*L)
+    De_spline = CubicSpline(Te_trans, De_interp)
+    De_Te_spline = CubicSpline.derivative(De_spline)
+    diffusivity = Diffusivity(interpolate = True, D_expression = De_spline, D_T_expression = De_Te_spline)
+    diffList.append(diffusivity)
+
+    Ns = 6
+    for i in range(1, Ns):
+        diffList.append(Diffusivity(interpolate = False))
+
+    params.diffusivityList = diffList
+
+    muList = []
+    Nmue_v_Te = transport["mobility"]
+    mue_interp = (Nmue_v_Te[:,1]/nAr)*V0*tau/(L*L)
+    mue_spline = CubicSpline(Te_trans, mue_interp)
+    mue_Te_spline = CubicSpline.derivative(mue_spline)
+    mobility = Mobility(interpolate = True, mu_expression = mue_spline, mu_T_expression = mue_Te_spline)
+    muList.append(mobility)
+
+    Ns = 6
+    for i in range(1, Ns):
+        muList.append(Mobility(interpolate = False))
+
+    params.mobilityList = muList
+    
+    # 5) Dump to screen
+    params.print()
diff --git a/psaapProperties_6Species_1Torr_Expanded.py b/psaapProperties_6Species_1Torr_Expanded.py
new file mode 100755
index 000000000..192237450
--- /dev/null
+++ b/psaapProperties_6Species_1Torr_Expanded.py
@@ -0,0 +1,552 @@
+import numpy as np
+from scipy.interpolate import CubicSpline
+import csv
+#import matplotlib.pyplot as plt
+#import matplotlib.colors as mcolors
+import h5py as h5
+
+import logging
+
+class Reaction(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Diffusivity(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Mobility(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+
+def setPsaapProperties_6Species_1Torr_Expanded(gam, inputV0, inputVDC, params, Nr, iSample):
+    """Sets non-dimensional properties corresponding to Liu 2014 paper.
+
+    Inputs:
+      gam : Secondary electron emission coefficient
+      params : chebSolver.modelParams class
+
+    Outputs: None
+      params data is overwritten using values from Liu 2014.
+    """
+    ###################################################################
+    # User specified parameters (you may change these if you wish to
+    # run a different scenario from Liu 2014)
+    ###################################################################
+
+    # densities
+    nAr = 3.22e22     # background number density of Ar [1/m^3] (corresponds to p = 1 Torr)
+    np0 = 8e16        # "nominal" electron density [1/m^3]
+
+    # masses
+    # me = 9.10938356e-31        # mass of an electron [kg]
+    # me = 5.489e-4              # mass of an electron [u]
+    me = 0.511e6                 # mass of an electron [eV/c2]
+    # mAr = 39.948               # mass of an argon atom [u]
+    # mAr = 39.948 * 1.66054e-27 # mass of an argon atom [kg]
+    mAr = 37.2158e9              # mass of an argon atom [eV/c2]
+    # u = 931.4941e6             # eV/c2
+    c = 299792458                # speed of light [m/s]
+    se = 40                      # momentum cross section [A^2]
+
+    # nominal electron energy
+    e0 = 1.0  # [eV]
+
+    # pressure
+    p  = 133.3*1.5      # [J/m^3] *1.5 to convert it to energy (1 Torr)
+
+    # gas energy at the wall
+    Tg0 = 0.038778    # 3/2*300K*kB ~ (p0 - nT[:,0])/ntot
+
+    # characteristics of driving voltage
+    V0  = inputV0                 # amplitude of driving voltage [V]
+    verticalShift = inputVDC      # DC voltage (vertical shift in driving voltage)
+    tau = (1./13.56e6)             # period of driving voltage [s]
+    L   = 2.00*0.005              # half-gap-width [m] (gap width is 2 cm)
+    electrodeArea = np.pi*0.05**2 # electrode area [m^2] (electrode diameter = 0.1 m)
+
+    # transport parameters
+    nmue = 9.66e21   # argon number density times electron mobility [1/(V*cm*s)]
+    nmum = 0.0
+    nmur = 0.0
+    nmu4p = 0.0
+    nmui = 8.0e19
+    nDe  = 3.86e22   # argon number density times electron diffusivity [1/(cm*s)]
+    nDi  = 2.07e18   # argon number density times ion diffusivity [1/(cm*s)]
+    nDm  = 2.42e18   # argon number density times AR(m) diffusivity [1/(cm*s)]
+    nDr  = 2.42e18
+    nD4p = 2.42e18
+
+    # reaction parameters (NB: k_i = Ck*Ee^B*exp(-A/Ee))
+    #                          Ee = 3/2*Te (Te in eV)
+    #                          -> k_i = [Ck*(2/3)^B] * Ee^B * exp[-(3/2)*A/Ee]
+    # nominal
+    Ck = np.array([2.0e-13,2.1e-15,5.0e-16,6.4e-16,2.1e-21,1.32e8,1.72e7,1.50e7,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0e-18,4.0e-19,0.0,0.0,0.0,0.0,2.5e-17,2.5e-17,1.0e-15,1.0e-15,0.0,0.0]) # pre-exponential factors [m^3/s]
+    B  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-0.5,0,0,0,0,0,0,0,0,0,0]) # Temperature Power
+    A  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]) # activation temperature [eV]
+    dH = np.array([0.0,-7.541,-10.577,-7.393,0.0,0.0,0.0,0.0,11.577,11.725,13.168,15.76,-11.577,4.183,0.148,1.592,2.592,-1.444,-1.592,-11.725,-0.148,1.444,0.0,0.0,-4.183,-4.035,-2.592,-15.76,0.0,0.0,-8.985,-9.133,4.035,-13.168]) # energy lost per electron due to ionization rxn [eV]
+    dEps = np.array([0.0,15.76,11.577,11.725,13.168,0.0]) # E, AR+, AR(m), AR(r), AR(4p), AR
+
+    # BC parameters
+    # ks = 1.19e7  # electron recombination rate [cm/s]
+    ks = 1.366109824889323e7 # electron recombination rate [cm/s/eV]
+
+    ###################################################################
+    # Constants of nature (probably shouldn't change unless you have
+    # root privileges on universe)
+    ###################################################################
+    qe   = 1.6e-19   # unit charge [C]
+    eps0 = 8.86e-12  # permittivity of free space [F/m]
+    kB   = 1.38e-23  # Boltzmann constant [J/K]
+    # kB   = 8.62e-5 # Boltzmann constant [eV/K]
+
+
+    ###################################################################
+    # Calculate non-dimensional parameters
+    ###################################################################
+
+    # 1) Convert input units to base SI (except eV)
+    nDe  *= 100. # 1/(m*s)
+    nDi  *= 100. # 1/(m*s)
+    nDm  *= 100.
+    nDr  *= 100.
+    nD4p *= 100.
+    nmue *= 100. # 1/(V*m*s)
+    nmui *= 100. # 1/(V*m*s)
+    ks   *= 0.01 # m/s
+    se   *= 1.0e-20  # m^2
+
+    # 2) Compute "raw" transport parameters
+    De  = nDe/nAr
+    Di  = nDi/nAr
+    Dm  = nDm/nAr
+    Dr  = nDr/nAr
+    D4p = nD4p/nAr
+
+    mue = nmue/nAr
+    mui = nmui/nAr
+    mum = nmum/nAr
+    mur = nmur/nAr
+    mu4p = nmu4p/nAr
+
+    # 3) Compute non-dimensional properties required by solver
+    De    = De*tau/(L*L)
+    Di    = Di*tau/(L*L)
+    Dm    = Dm*tau/(L*L)
+    Dr    = Dr*tau/(L*L)
+    D4p   = D4p*tau/(L*L)
+
+    mue   = mue*V0*tau/(L*L)
+    mui   = mui*V0*tau/(L*L)
+    mum   = mum*V0*tau/(L*L)
+    mur   = mur*V0*tau/(L*L)
+    mu4p  = mu4p*V0*tau/(L*L)
+
+    Ck[0:4]  *= tau*np0
+    Ck[4]    *= tau*nAr
+    Ck[5:8]  *= tau
+    Ck[8:12] *= tau*nAr
+    Ck[12:24]  *= tau*np0
+    Ck[24:28] *= tau*np0*np0
+    Ck[28:30] *= tau*nAr
+    Ck[30:] *= tau*np0
+    A        = A*1.5/e0  # 1.5 to convert from temperature to energy
+    dH       = dH/e0
+    qStar    = V0/e0 # qe*V0/e0, since e0 in eV, need qe*V0 in eV, which is just V0 in V
+    alpha    = qe*np0*L*L/(V0*eps0)
+    ks       = ks*tau/L
+    p0       = p/qe/np0
+    kappaB   = 4.878171165833662*1.6129 # non-dimensional thermal conductivity of background specie
+                                # (2/3)*tau/L**2*Kb/np0/kB,
+                                # where Kb is the thermal conductivity of background specie
+
+    params.beta = np.array([[0,1,1,1,0,0,0,0,1,1,1,2,1,2,1,1,2,1,1,1,1,1,0,0,1,1,1,1,0,0,1,1,2,1],                     # E
+                            [0,1,1,1,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0],                     # AR+
+                            [0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,1,0,0,0,0,0],                     # AR(m)
+                            [0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0],                     # AR(r)
+                            [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0],                     # AR(4p)
+                            [2,1,1,1,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1]], dtype=np.int64)    # AR
+
+    params.alfa = np.array([[0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,0,0,0,0,1,1],                     # E
+                            [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0],                     # AR+
+                            [2,1,0,2,1,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],                     # AR(m)
+                            [0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,1,0],                     # AR(r)
+                            [0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,0,1],                     # AR(4p)
+                            [0,0,0,0,1,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0]], dtype=np.int64)    # AR
+    
+    # Rxn1:  2AR(m)         ->   2AR
+    # Rxn2:  AR(m) + AR(r)  ->   E + AR+ + AR
+    # Rxn3:  2AR(4p)        ->   E + AR+ + AR
+    # Rxn4:  2AR(m)         ->   E + AR+ + AR
+    # Rxn5:  AR(m) + AR     ->   2AR
+    # Rxn6:  AR(r)          ->   AR + hv
+    # Rxn7:  AR(4p)         ->   AR(m) + hv
+    # Rxn8:  AR(4p)         ->   AR(r) + hv
+    # Rxn9:  E + AR         ->   E + AR(m)
+    # Rxn10: E + AR         ->   E + AR(r)
+    # Rxn11: E + AR         ->   E + AR(4p)
+    # Rxn12: E + AR         ->   2E + AR+
+    # Rxn13: E + AR(m)      ->   E + AR
+    # Rxn14: E + AR(m)      ->   2E + AR+
+    # Rxn15: E + AR(m)      ->   E + AR(r)
+    # Rxn16: E + AR(m)      ->   E + AR(4p)
+    # Rxn17: E + AR(4p)     ->   2E + AR+
+    # Rxn18: E + AR(4p)     ->   E + AR(r)
+    # Rxn19: E + AR(4p)     ->   E + AR(m)
+    # Rxn20: E + AR(r)      ->   E + AR
+    # Rxn21: E + AR(r)      ->   E + AR(m)
+    # Rxn22: E + AR(r)      ->   E + AR(4p)
+    # Rxn23: E + AR+        ->   AR(m) + hv
+    # Rxn24: E + AR+        ->   AR(4p) + hv
+    # Rxn25: 2E + AR+       ->   E + AR(m)
+    # Rxn26: 2E + AR+       ->   E + AR(r)
+    # Rxn27: 2E + AR+       ->   E + AR(4p)
+    # Rxn28: 2E + AR+       ->   E + AR
+    # Rxn29: AR + AR(4p)    ->   AR + AR(m)
+    # Rxn30: AR + AR(4p)    ->   AR + AR(r)
+    # Rxn31: AR(m) + AR(4p) ->   E + AR+ + AR
+    # Rxn32: AR(r) + AR(4p) ->   E + AR+ + AR
+    # Rxn33: E + AR(r)      ->   2E + AR+
+    # Rxn34: E + AR(4p)     ->   E + AR
+
+    rxnNameDict = { 0: "2Ar(m) => 2Ar",
+                    1: "Ar(m) + Ar(r) => E + Ar+ + Ar",
+                    2: "2Ar(4p) => E + Ar+ + Ar",
+                    3: "2Ar(m) => E + Ar+ + Ar",
+                    4: "Ar(m) + Ar => 2Ar",
+                    5: "Ar(r) => Ar",
+                    6: "Ar(4p) => Ar(m)",
+                    7: "Ar(4p) => Ar(r)",
+                    8: "Excitation_Metastable",
+                    9: "Excitation_Resonant",
+                   10: "Excitation_4p",
+                   11: "Ionization",
+                   12: "DeExcitation_Metastable",
+                   13: "StepIonization_Metastable",
+                   14: "E + Ar(m) => E + Ar(r)",
+                   15: "E + Ar(m) => E + Ar(4p)",
+                   16: "StepIonization_4p",
+                   17: "E + Ar(4p) => E + Ar(r)",
+                   18: "E + Ar(4p) => E + Ar(m)",
+                   19: "DeExcitation_Resonant",
+                   20: "E + Ar(r) => E + Ar(m)",
+                   21: "E + Ar(r) => E + Ar(4p)",
+                   22: "E + Ar+ => Ar(m)",
+                   23: "E + Ar+ => Ar(4p)",
+                   24: "3BdyRecomb_Metastable",
+                   25: "3BdyRecomb_Resonant",
+                   26: "3BdyRecomb_4p",
+                   27: "3BdyRecomb_Ground",
+                   28: "Ar + Ar(4p) => Ar + Ar(m)",
+                   29: "Ar + Ar(4p) => Ar + Ar(r)",
+                   30: "Ar(m) + Ar(4p) => E + Ar+ + Ar",
+                   31: "Ar(r) + Ar(4p) => E + Ar+ + Ar",
+                   32: "StepIonization_Resonant",
+                   33: "DeExcitation_4p"}
+
+    # 4) Set values in params class
+    params.D[0]    = De
+    params.D[1]    = Di
+    params.D[2]    = Dm
+    params.D[3]    = Dr
+    params.D[4]    = D4p
+
+    params.mu[0]   = mue
+    params.mu[1]   = mui
+    params.mu[2]   = mum
+    params.mu[3]   = mur
+    params.mu[4]   = mu4p
+
+    params.A[:]    = Ck[:]
+    params.B[:]    = B[:]
+    params.C[:]    = A[:]
+
+    # Account for the 2/3 term to convert from electron temperature to electron energy
+    for i in range(len(params.A)):
+        params.A[i] *= (2/3)**(params.B[i])
+
+    params.dH[:]   = dH[:]
+    params.dEps[:] = dEps[:]
+    params.qStar   = qStar
+    params.alpha   = alpha
+    params.ks      = ks
+    params.gam     = gam
+    params.kappaB  = kappaB
+    params.nAronp0 = nAr / np0
+    params.p0      = p0
+    params.Tg0     = Tg0
+    params.EC      = 2.0 * me / mAr \
+        * np.sqrt(16.0 * (me + mAr) * e0 * c**2
+                  / (3.0 * np.pi * me * mAr)) * se * nAr * tau
+    # params.EC = 2.0 * me / mAr * 3.8e9 * tau
+
+    params.verticalShift = verticalShift / V0
+
+    # Parameters needed to compute the current with dimensions
+    params.V0Ltau  = V0 / (L * tau)
+    params.V0L     = V0 / L
+    params.LLV0tau = (L*L) / (V0*tau)
+    params.tauL    = L / tau
+    params.np0     = np0           # "nominal" electron density [1/m^3]
+    params.qe      = qe            # unit charge [C]
+    params.eps0    = eps0          # unit charge [C]
+    params.eArea   = electrodeArea # electrode area [m^2]
+
+
+    reactionExpressionslist = [f"{params.A[0]} * energy**{params.B[0]} * np.exp(-{params.C[0]} / energy)",
+                               f"{params.A[1]} * energy**{params.B[1]} * np.exp(-{params.C[1]} / energy)",
+                               f"{params.A[2]} * energy**{params.B[2]} * np.exp(-{params.C[2]} / energy)",
+                               f"{params.A[3]} * energy**{params.B[3]} * np.exp(-{params.C[3]} / energy)",
+                               f"{params.A[4]} * energy**{params.B[4]} * np.exp(-{params.C[4]} / energy)",
+                               f"{params.A[5]} * energy**{params.B[5]} * np.exp(-{params.C[5]} / energy)",
+                               f"{params.A[6]} * energy**{params.B[6]} * np.exp(-{params.C[6]} / energy)",
+                               f"{params.A[7]} * energy**{params.B[7]} * np.exp(-{params.C[7]} / energy)",
+                               f"{params.A[8]} * energy**{params.B[8]} * np.exp(-{params.C[8]} / energy)",
+                               f"{params.A[9]} * energy**{params.B[9]} * np.exp(-{params.C[9]} / energy)",
+                               f"{params.A[10]} * energy**{params.B[10]} * np.exp(-{params.C[10]} / energy)",
+                               f"{params.A[11]} * energy**{params.B[11]} * np.exp(-{params.C[11]} / energy)",
+                               f"{params.A[12]} * energy**{params.B[12]} * np.exp(-{params.C[12]} / energy)",
+                               f"{params.A[13]} * energy**{params.B[13]} * np.exp(-{params.C[13]} / energy)",
+                               f"{params.A[14]} * energy**{params.B[14]} * np.exp(-{params.C[14]} / energy)",
+                               f"{params.A[15]} * energy**{params.B[15]} * np.exp(-{params.C[15]} / energy)",
+                               f"{params.A[16]} * energy**{params.B[16]} * np.exp(-{params.C[16]} / energy)",
+                               f"{params.A[17]} * energy**{params.B[17]} * np.exp(-{params.C[17]} / energy)",
+                               f"{params.A[18]} * energy**{params.B[18]} * np.exp(-{params.C[18]} / energy)",
+                               f"{params.A[19]} * energy**{params.B[19]} * np.exp(-{params.C[19]} / energy)",
+                               f"{params.A[20]} * energy**{params.B[20]} * np.exp(-{params.C[20]} / energy)",
+                               f"{params.A[21]} * energy**{params.B[21]} * np.exp(-{params.C[21]} / energy)",
+                               f"{params.A[22]} * energy**{params.B[22]} * np.exp(-{params.C[22]} / energy)",
+                               f"{params.A[23]} * energy**{params.B[23]} * np.exp(-{params.C[23]} / energy)",
+                               f"{params.A[24]} * energy**{params.B[24]} * np.exp(-{params.C[24]} / energy)",
+                               f"{params.A[25]} * energy**{params.B[25]} * np.exp(-{params.C[25]} / energy)",
+                               f"{params.A[26]} * energy**{params.B[26]} * np.exp(-{params.C[26]} / energy)",
+                               f"{params.A[27]} * energy**{params.B[27]} * np.exp(-{params.C[27]} / energy)",
+                               f"{params.A[28]} * energy**{params.B[28]} * np.exp(-{params.C[28]} / energy)",
+                               f"{params.A[29]} * energy**{params.B[29]} * np.exp(-{params.C[29]} / energy)",
+                               f"{params.A[30]} * energy**{params.B[30]} * np.exp(-{params.C[30]} / energy)",
+                               f"{params.A[31]} * energy**{params.B[31]} * np.exp(-{params.C[31]} / energy)",
+                               f"{params.A[32]} * energy**{params.B[32]} * np.exp(-{params.C[32]} / energy)",
+                               f"{params.A[33]} * energy**{params.B[33]} * np.exp(-{params.C[33]} / energy)"]
+
+
+    reactionTExpressionslist = [f"{params.A[0]} * (energy**({params.B[0]}-1)) * np.exp(-{params.C[0]}/energy) * ({params.B[0]} + {params.C[0]}/energy)",
+                                f"{params.A[1]} * (energy**({params.B[1]}-1)) * np.exp(-{params.C[1]}/energy) * ({params.B[1]} + {params.C[1]}/energy)",
+                                f"{params.A[2]} * (energy**({params.B[2]}-1)) * np.exp(-{params.C[2]}/energy) * ({params.B[2]} + {params.C[2]}/energy)",
+                                f"{params.A[3]} * (energy**({params.B[3]}-1)) * np.exp(-{params.C[3]}/energy) * ({params.B[3]} + {params.C[3]}/energy)",
+                                f"{params.A[4]} * (energy**({params.B[4]}-1)) * np.exp(-{params.C[4]}/energy) * ({params.B[4]} + {params.C[4]}/energy)",
+                                f"{params.A[5]} * (energy**({params.B[5]}-1)) * np.exp(-{params.C[5]}/energy) * ({params.B[5]} + {params.C[5]}/energy)",
+                                f"{params.A[6]} * (energy**({params.B[6]}-1)) * np.exp(-{params.C[6]}/energy) * ({params.B[6]} + {params.C[6]}/energy)",
+				f"{params.A[7]} * (energy**({params.B[7]}-1)) * np.exp(-{params.C[7]}/energy) * ({params.B[7]} + {params.C[7]}/energy)",
+		                f"{params.A[8]} * (energy**({params.B[8]}-1)) * np.exp(-{params.C[8]}/energy) * ({params.B[8]} + {params.C[8]}/energy)",
+                                f"{params.A[9]} * (energy**({params.B[9]}-1)) * np.exp(-{params.C[9]}/energy) * ({params.B[9]} + {params.C[9]}/energy)",
+                                f"{params.A[10]} * (energy**({params.B[10]}-1)) * np.exp(-{params.C[10]}/energy) * ({params.B[10]} + {params.C[10]}/energy)",
+                                f"{params.A[11]} * (energy**({params.B[11]}-1)) * np.exp(-{params.C[11]}/energy) * ({params.B[11]} + {params.C[11]}/energy)",
+                                f"{params.A[12]} * (energy**({params.B[12]}-1)) * np.exp(-{params.C[12]}/energy) * ({params.B[12]} + {params.C[12]}/energy)",
+                                f"{params.A[13]} * (energy**({params.B[13]}-1)) * np.exp(-{params.C[13]}/energy) * ({params.B[13]} + {params.C[13]}/energy)",
+                                f"{params.A[14]} * (energy**({params.B[14]}-1)) * np.exp(-{params.C[14]}/energy) * ({params.B[14]} + {params.C[14]}/energy)",
+                                f"{params.A[15]} * (energy**({params.B[15]}-1)) * np.exp(-{params.C[15]}/energy) * ({params.B[15]} + {params.C[15]}/energy)",
+                                f"{params.A[16]} * (energy**({params.B[16]}-1)) * np.exp(-{params.C[16]}/energy) * ({params.B[16]} + {params.C[16]}/energy)",
+                                f"{params.A[17]} * (energy**({params.B[17]}-1)) * np.exp(-{params.C[17]}/energy) * ({params.B[17]} + {params.C[17]}/energy)",
+                                f"{params.A[18]} * (energy**({params.B[18]}-1)) * np.exp(-{params.C[18]}/energy) * ({params.B[18]} + {params.C[18]}/energy)",
+                                f"{params.A[19]} * (energy**({params.B[19]}-1)) * np.exp(-{params.C[19]}/energy) * ({params.B[19]} + {params.C[19]}/energy)",
+                                f"{params.A[20]} * (energy**({params.B[20]}-1)) * np.exp(-{params.C[20]}/energy) * ({params.B[20]} + {params.C[20]}/energy)",
+                                f"{params.A[21]} * (energy**({params.B[21]}-1)) * np.exp(-{params.C[21]}/energy) * ({params.B[21]} + {params.C[21]}/energy)",
+                                f"{params.A[22]} * (energy**({params.B[22]}-1)) * np.exp(-{params.C[22]}/energy) * ({params.B[22]} + {params.C[22]}/energy)",
+                                f"{params.A[23]} * (energy**({params.B[23]}-1)) * np.exp(-{params.C[23]}/energy) * ({params.B[23]} + {params.C[23]}/energy)",
+                                f"{params.A[24]} * (energy**({params.B[24]}-1)) * np.exp(-{params.C[24]}/energy) * ({params.B[24]} + {params.C[24]}/energy)",
+                                f"{params.A[25]} * (energy**({params.B[25]}-1)) * np.exp(-{params.C[25]}/energy) * ({params.B[25]} + {params.C[25]}/energy)",
+                                f"{params.A[26]} * (energy**({params.B[26]}-1)) * np.exp(-{params.C[26]}/energy) * ({params.B[26]} + {params.C[26]}/energy)",
+                                f"{params.A[27]} * (energy**({params.B[27]}-1)) * np.exp(-{params.C[27]}/energy) * ({params.B[27]} + {params.C[27]}/energy)",
+                                f"{params.A[28]} * (energy**({params.B[28]}-1)) * np.exp(-{params.C[28]}/energy) * ({params.B[28]} + {params.C[28]}/energy)",
+                                f"{params.A[29]} * (energy**({params.B[29]}-1)) * np.exp(-{params.C[29]}/energy) * ({params.B[29]} + {params.C[29]}/energy)",
+                                f"{params.A[30]} * (energy**({params.B[30]}-1)) * np.exp(-{params.C[30]}/energy) * ({params.B[30]} + {params.C[30]}/energy)",
+                                f"{params.A[31]} * (energy**({params.B[31]}-1)) * np.exp(-{params.C[31]}/energy) * ({params.B[31]} + {params.C[31]}/energy)",
+                                f"{params.A[32]} * (energy**({params.B[32]}-1)) * np.exp(-{params.C[32]}/energy) * ({params.B[32]} + {params.C[32]}/energy)",
+                                f"{params.A[33]} * (energy**({params.B[33]}-1)) * np.exp(-{params.C[33]}/energy) * ({params.B[33]} + {params.C[33]}/energy)"]
+           
+
+    reactionExpressionTypelist =  np.array([False,False,False,False,False,False,False,False, # Rxns 1-8
+                                           True,True,True,True,True,True,True,True,True,True,True,True,True,True, # Rxns 9-22
+                                           False,False,True,True,True,True,False,False,False,False,True,True]) # Rxns 23-34
+    
+    thresholded_rxn = np.array([False, False, False, False, False, False, False, False,
+                                True, True, True, True, False, True, True, True, True, False, False, False, False,
+                                True, False, False, False, False, False, False, False, False, False, False, True,
+                                False])
+    reactionsList = []
+    LOGFilename = 'interpolationSample.log'
+    f = open(LOGFilename, 'w')
+
+    for i in range(Nr):
+        if reactionExpressionTypelist[i]:
+            if i < 14 or i == 16 or i == 19 or i > 23:
+                f = h5.File("../../../BOLSIGChemistry_NominalRates/{0:s}.h5".format(rxnNameDict[i]), 'r')
+                dataset = f["table"]
+            else:
+                f = h5.File("../../../BOLSIGChemistry_NominalRates/StepExcitation.h5", 'r')
+                dataset = f[rxnNameDict[i]]
+
+            Te = dataset[:,0]
+            Te /= 11604
+            rateCoeff = dataset[:,1]
+            if i > 23 and i < 28:
+                rateCoeff /= 6.022e23**2
+            else:
+                rateCoeff /= 6.022e23
+
+            ## Removing BOLSIG failures
+            fail_inds = []
+            for j in range(len(rateCoeff)):
+                if rateCoeff[j] == 0.0 and j > np.nonzero(rateCoeff)[0][0]:
+                    fail_inds.append(j)
+
+            Te = np.delete(Te, fail_inds)
+            rateCoeff = np.delete(rateCoeff, fail_inds)
+
+            # Sorting mean energy array and rate coefficient array based on
+            # the mean energy array.
+            Teinds = Te.argsort()
+            rateCoeff = rateCoeff[Teinds]
+            Te = Te[Teinds]
+
+            # Find duplicates
+            TeDuplicateinds = np.where(np.abs(np.diff(Te, axis=0)) > 0.0)
+            TeDuplicateindsForLog = np.where(np.abs(np.diff(Te, axis=0)) == 0.0)
+            rateCoeff = rateCoeff[TeDuplicateinds]
+            Te = Te[TeDuplicateinds]
+
+            # Nondimensionalization of mean energy.
+            Te *= 1.5
+
+            # Find first non-zero value of the coefficient rate.
+            I = np.nonzero(rateCoeff)
+
+            diffRateCoeff = [j-i for i, j in zip(rateCoeff[:-1], rateCoeff[1:])]
+            diffTe = [j-i for i, j in zip(Te[:-1], Te[1:])]
+
+            Monotonicity = np.asarray([j/i for i, j in zip(diffTe, diffRateCoeff)])
+            Monotonicity = np.insert(Monotonicity, 0, 0.0, axis=0)
+            print("RXN {}:".format(i+1))
+            print(Monotonicity)
+            print("")
+
+            Nan = np.isnan(Monotonicity)
+            Inf = np.isinf(Monotonicity)
+            if thresholded_rxn[i] == True:
+                indexPositive = np.where(Monotonicity>0.0)
+            else:
+                indexPositive = np.where(Monotonicity<0.0)
+            Positive = np.full(Monotonicity.shape, False, dtype=bool)
+            Positive[indexPositive] = True
+            
+            indices = Nan + Inf + Positive
+
+
+            #lastFalse = np.where(indices==False)[-1][-1] + 2
+            for k in range(len(Te)):
+               if Te[k] < 4.5 and indices[k] == False:
+                  lastFalse = k + 2
+            #lastFalse = np.nonzero(rateCoeff)[0][0]
+            # Transformation to log scale.
+            TeLog = np.log(Te)
+
+            # Compute the slope of the rate coefficient between its first two non-zero values.
+            # Finite differences are used.
+            dydx = (rateCoeff[lastFalse + 1] - rateCoeff[lastFalse]) \
+                 / (Te[lastFalse + 1] - Te[lastFalse])
+
+            # Arrhenius form: kf = A * exp(-C / Te)
+            C = Te[lastFalse]**2.0*dydx / rateCoeff[lastFalse]
+
+            # Compute pre-exponential coefficient, A, in log scale.
+            ALog = np.log(rateCoeff[lastFalse]) + C / Te[lastFalse]
+
+            # Transform rate coefficient in log scale.
+            rateCoeffLog = np.zeros(rateCoeff.shape)
+            rateCoeffLog[lastFalse:] = np.log(rateCoeff[lastFalse:])
+            # For the troublesome values, we use the Arrhenius form.
+            rateCoeffLog[0:lastFalse] = ALog - C / Te[0:lastFalse]
+            # Nondimensionalization in log scale.
+            if (i < 12):
+                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
+            elif i > 23 and i < 28:
+                rateCoeffLog += - np.log(1.0/tau) + 2*np.log(np0)
+            else:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(np0)
+
+            # Interpolation in log scale.
+            reactionExpressionsLog = CubicSpline(TeLog, rateCoeffLog)
+            # Gradient in log scale
+            reactionTExpressionsLog = CubicSpline.derivative(reactionExpressionsLog)
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf_log = reactionExpressionsLog,
+                                kf_T_log = reactionTExpressionsLog)
+            reactionsList.append(reaction)
+
+            logging.basicConfig(filename=LOGFilename)
+            logging.warning('Interpolation info (Reaction %s):', i + 1)
+            logging.warning('First non-zero entry in the rate coefficient: %s', I[0][0])
+            logging.warning('Monotonicity of the rate coefficient start from entry: %s', lastFalse)
+            logging.warning('Position of possible duplicates in mean energy array: %s',
+                          TeDuplicateindsForLog[0])
+
+        else:
+            rxn   = eval("lambda energy :" + reactionExpressionslist[i])
+            rxn_T = eval("lambda energy :" + reactionTExpressionslist[i])
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf = rxn, kf_T = rxn_T)
+            reactionsList.append(reaction)
+
+    params.reactionsList = reactionsList
+
+    ## Electron Transport Data
+    diffList = []
+    transport = h5.File("../../../BOLSIGChemistry_NominalRates/nominal_transport.h5", 'r')
+    NDe_v_Te = transport["diffusivity"]
+    Te_trans = NDe_v_Te[:,0]
+    Te_trans /= 11604
+    De_interp = (NDe_v_Te[:,1]/nAr)*tau/(L*L)
+    De_spline = CubicSpline(Te_trans, De_interp)
+    De_Te_spline = CubicSpline.derivative(De_spline)
+    diffusivity = Diffusivity(interpolate = True, D_expression = De_spline, D_T_expression = De_Te_spline)
+    diffList.append(diffusivity)
+
+    Ns = 6
+    for i in range(1, Ns):
+        diffList.append(Diffusivity(interpolate = False))
+
+    params.diffusivityList = diffList
+
+    muList = []
+    Nmue_v_Te = transport["mobility"]
+    mue_interp = (Nmue_v_Te[:,1]/nAr)*V0*tau/(L*L)
+    mue_spline = CubicSpline(Te_trans, mue_interp)
+    mue_Te_spline = CubicSpline.derivative(mue_spline)
+    mobility = Mobility(interpolate = True, mu_expression = mue_spline, mu_T_expression = mue_Te_spline)
+    muList.append(mobility)
+
+    Ns = 6
+    for i in range(1, Ns):
+        muList.append(Mobility(interpolate = False))
+
+    params.mobilityList = muList
+    
+    # 5) Dump to screen
+    params.print()
diff --git a/psaapProperties_6Species_1Torr_Old.py b/psaapProperties_6Species_1Torr_Old.py
new file mode 100755
index 000000000..79b313a1b
--- /dev/null
+++ b/psaapProperties_6Species_1Torr_Old.py
@@ -0,0 +1,542 @@
+import numpy as np
+from scipy.interpolate import CubicSpline
+import csv
+#import matplotlib.pyplot as plt
+#import matplotlib.colors as mcolors
+import h5py as h5
+
+import logging
+
+class Reaction(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Diffusivity(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Mobility(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+
+def setPsaapProperties_6Species_1Torr_Expanded(gam, inputV0, inputVDC, params, Nr, iSample):
+    """Sets non-dimensional properties corresponding to Liu 2014 paper.
+
+    Inputs:
+      gam : Secondary electron emission coefficient
+      params : chebSolver.modelParams class
+
+    Outputs: None
+      params data is overwritten using values from Liu 2014.
+    """
+    ###################################################################
+    # User specified parameters (you may change these if you wish to
+    # run a different scenario from Liu 2014)
+    ###################################################################
+
+    # densities
+    nAr = 3.22e22     # background number density of Ar [1/m^3] (corresponds to p = 1 Torr)
+    np0 = 8e16        # "nominal" electron density [1/m^3]
+
+    # masses
+    # me = 9.10938356e-31        # mass of an electron [kg]
+    # me = 5.489e-4              # mass of an electron [u]
+    me = 0.511e6                 # mass of an electron [eV/c2]
+    # mAr = 39.948               # mass of an argon atom [u]
+    # mAr = 39.948 * 1.66054e-27 # mass of an argon atom [kg]
+    mAr = 37.2158e9              # mass of an argon atom [eV/c2]
+    # u = 931.4941e6             # eV/c2
+    c = 299792458                # speed of light [m/s]
+    se = 40                      # momentum cross section [A^2]
+
+    # nominal electron energy
+    e0 = 1.0  # [eV]
+
+    # pressure
+    p  = 133.3*1.5      # [J/m^3] *1.5 to convert it to energy (1 Torr)
+
+    # gas energy at the wall
+    Tg0 = 0.038778    # 3/2*300K*kB ~ (p0 - nT[:,0])/ntot
+
+    # characteristics of driving voltage
+    V0  = inputV0                 # amplitude of driving voltage [V]
+    verticalShift = inputVDC      # DC voltage (vertical shift in driving voltage)
+    tau = (1./13.56e6)             # period of driving voltage [s]
+    L   = 2.00*0.005              # half-gap-width [m] (gap width is 2 cm)
+    electrodeArea = np.pi*0.05**2 # electrode area [m^2] (electrode diameter = 0.1 m)
+
+    # transport parameters
+    nmue = 9.66e21   # argon number density times electron mobility [1/(V*cm*s)]
+    nmum = 0.0
+    nmur = 0.0
+    nmu4p = 0.0
+    nmui = 8.0e19
+    nDe  = 3.86e22   # argon number density times electron diffusivity [1/(cm*s)]
+    nDi  = 2.07e18   # argon number density times ion diffusivity [1/(cm*s)]
+    nDm  = 2.42e18   # argon number density times AR(m) diffusivity [1/(cm*s)]
+    nDr  = 2.42e18
+    nD4p = 2.42e18
+
+    # reaction parameters (NB: k_i = Ck*Ee^B*exp(-A/Ee))
+    #                          Ee = 3/2*Te (Te in eV)
+    #                          -> k_i = [Ck*(2/3)^B] * Ee^B * exp[-(3/2)*A/Ee]
+    # nominal
+    Ck = np.array([2.0e-13,2.1e-15,5.0e-16,6.4e-16,2.1e-21,1.32e8,1.72e7,1.50e7,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0e-18,4.0e-19,0.0,0.0,0.0,0.0,2.5e-17,2.5e-17,1.0e-15,1.0e-15,0.0,0.0]) # pre-exponential factors [m^3/s]
+    B  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-0.5,0,0,0,0,0,0,0,0,0,0]) # Temperature Power
+    A  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]) # activation temperature [eV]
+    dH = np.array([0.0,-7.541,-10.577,-7.393,0.0,0.0,0.0,0.0,11.577,11.725,13.168,15.76,-11.577,4.183,0.148,1.592,2.592,-1.444,-1.592,-11.725,-0.148,1.444,0.0,0.0,-4.183,-4.035,-2.592,-15.76,0.0,0.0,-8.985,-9.133,4.035,-13.168]) # energy lost per electron due to ionization rxn [eV]
+    dEps = np.array([0.0,15.76,11.577,11.725,13.168,0.0]) # E, AR+, AR(m), AR(r), AR(4p), AR
+
+    # BC parameters
+    # ks = 1.19e7  # electron recombination rate [cm/s]
+    ks = 1.366109824889323e7 # electron recombination rate [cm/s/eV]
+
+    ###################################################################
+    # Constants of nature (probably shouldn't change unless you have
+    # root privileges on universe)
+    ###################################################################
+    qe   = 1.6e-19   # unit charge [C]
+    eps0 = 8.86e-12  # permittivity of free space [F/m]
+    kB   = 1.38e-23  # Boltzmann constant [J/K]
+    # kB   = 8.62e-5 # Boltzmann constant [eV/K]
+
+
+    ###################################################################
+    # Calculate non-dimensional parameters
+    ###################################################################
+
+    # 1) Convert input units to base SI (except eV)
+    nDe  *= 100. # 1/(m*s)
+    nDi  *= 100. # 1/(m*s)
+    nDm  *= 100.
+    nDr  *= 100.
+    nD4p *= 100.
+    nmue *= 100. # 1/(V*m*s)
+    nmui *= 100. # 1/(V*m*s)
+    ks   *= 0.01 # m/s
+    se   *= 1.0e-20  # m^2
+
+    # 2) Compute "raw" transport parameters
+    De  = nDe/nAr
+    Di  = nDi/nAr
+    Dm  = nDm/nAr
+    Dr  = nDr/nAr
+    D4p = nD4p/nAr
+
+    mue = nmue/nAr
+    mui = nmui/nAr
+    mum = nmum/nAr
+    mur = nmur/nAr
+    mu4p = nmu4p/nAr
+
+    # 3) Compute non-dimensional properties required by solver
+    De    = De*tau/(L*L)
+    Di    = Di*tau/(L*L)
+    Dm    = Dm*tau/(L*L)
+    Dr    = Dr*tau/(L*L)
+    D4p   = D4p*tau/(L*L)
+
+    mue   = mue*V0*tau/(L*L)
+    mui   = mui*V0*tau/(L*L)
+    mum   = mum*V0*tau/(L*L)
+    mur   = mur*V0*tau/(L*L)
+    mu4p  = mu4p*V0*tau/(L*L)
+
+    Ck[0:4]  *= tau*np0
+    Ck[4]    *= tau*nAr
+    Ck[5:8]  *= tau
+    Ck[8:12] *= tau*nAr
+    Ck[12:24]  *= tau*np0
+    Ck[24:28] *= tau*np0*np0
+    Ck[28:30] *= tau*nAr
+    Ck[30:] *= tau*np0
+    A        = A*1.5/e0  # 1.5 to convert from temperature to energy
+    dH       = dH/e0
+    qStar    = V0/e0 # qe*V0/e0, since e0 in eV, need qe*V0 in eV, which is just V0 in V
+    alpha    = qe*np0*L*L/(V0*eps0)
+    ks       = ks*tau/L
+    p0       = p/qe/np0
+    kappaB   = 4.878171165833662*1.6129 # non-dimensional thermal conductivity of background specie
+                                # (2/3)*tau/L**2*Kb/np0/kB,
+                                # where Kb is the thermal conductivity of background specie
+
+    params.beta = np.array([[0,1,1,1,0,0,0,0,1,1,1,2,1,2,1,1,2,1,1,1,1,1,0,0,1,1,1,1,0,0,1,1,2,1],                     # E
+                            [0,1,1,1,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0],                     # AR+
+                            [0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,1,0,0,0,0,0],                     # AR(m)
+                            [0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0],                     # AR(r)
+                            [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0],                     # AR(4p)
+                            [2,1,1,1,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1]], dtype=np.int64)    # AR
+
+    params.alfa = np.array([[0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,0,0,0,0,1,1],                     # E
+                            [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0],                     # AR+
+                            [2,1,0,2,1,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],                     # AR(m)
+                            [0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,1,0],                     # AR(r)
+                            [0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,0,1],                     # AR(4p)
+                            [0,0,0,0,1,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0]], dtype=np.int64)    # AR
+    
+    # Rxn1:  2AR(m)         ->   2AR
+    # Rxn2:  AR(m) + AR(r)  ->   E + AR+ + AR
+    # Rxn3:  2AR(4p)        ->   E + AR+ + AR
+    # Rxn4:  2AR(m)         ->   E + AR+ + AR
+    # Rxn5:  AR(m) + AR     ->   2AR
+    # Rxn6:  AR(r)          ->   AR + hv
+    # Rxn7:  AR(4p)         ->   AR(m) + hv
+    # Rxn8:  AR(4p)         ->   AR(r) + hv
+    # Rxn9:  E + AR         ->   E + AR(m)
+    # Rxn10: E + AR         ->   E + AR(r)
+    # Rxn11: E + AR         ->   E + AR(4p)
+    # Rxn12: E + AR         ->   2E + AR+
+    # Rxn13: E + AR(m)      ->   E + AR
+    # Rxn14: E + AR(m)      ->   2E + AR+
+    # Rxn15: E + AR(m)      ->   E + AR(r)
+    # Rxn16: E + AR(m)      ->   E + AR(4p)
+    # Rxn17: E + AR(4p)     ->   2E + AR+
+    # Rxn18: E + AR(4p)     ->   E + AR(r)
+    # Rxn19: E + AR(4p)     ->   E + AR(m)
+    # Rxn20: E + AR(r)      ->   E + AR
+    # Rxn21: E + AR(r)      ->   E + AR(m)
+    # Rxn22: E + AR(r)      ->   E + AR(4p)
+    # Rxn23: E + AR+        ->   AR(m) + hv
+    # Rxn24: E + AR+        ->   AR(4p) + hv
+    # Rxn25: 2E + AR+       ->   E + AR(m)
+    # Rxn26: 2E + AR+       ->   E + AR(r)
+    # Rxn27: 2E + AR+       ->   E + AR(4p)
+    # Rxn28: 2E + AR+       ->   E + AR
+    # Rxn29: AR + AR(4p)    ->   AR + AR(m)
+    # Rxn30: AR + AR(4p)    ->   AR + AR(r)
+    # Rxn31: AR(m) + AR(4p) ->   E + AR+ + AR
+    # Rxn32: AR(r) + AR(4p) ->   E + AR+ + AR
+    # Rxn33: E + AR(r)      ->   2E + AR+
+    # Rxn34: E + AR(4p)     ->   E + AR
+
+    rxnNameDict = { 0: "2Ar(m) => 2Ar",
+                    1: "Ar(m) + Ar(r) => E + Ar+ + Ar",
+                    2: "2Ar(4p) => E + Ar+ + Ar",
+                    3: "2Ar(m) => E + Ar+ + Ar",
+                    4: "Ar(m) + Ar => 2Ar",
+                    5: "Ar(r) => Ar",
+                    6: "Ar(4p) => Ar(m)",
+                    7: "Ar(4p) => Ar(r)",
+                    8: "1s-metastable",
+                    9: "1s-resonance",
+                   10: "2p-lumped",
+                   11: "Ionization",
+                   12: "Deexci-metastable",
+                   13: "StepIonization",
+                   14: "E + Ar(m) => E + Ar(r)",
+                   15: "E + Ar(m) => E + Ar(4p)",
+                   16: "StepIonization",
+                   17: "E + Ar(4p) => E + Ar(r)",
+                   18: "E + Ar(4p) => E + Ar(m)",
+                   19: "Deexci-resonance",
+                   20: "E + Ar(r) => E + Ar(m)",
+                   21: "E + Ar(r) => E + Ar(4p)",
+                   22: "E + Ar+ => Ar(m)",
+                   23: "E + Ar+ => Ar(4p)",
+                   24: "3BdyRecomb-metastable",
+                   25: "3BdyRecomb-metastable",
+                   26: "3BdyRecomb-metastable",
+                   27: "3BdyRecomb-ground",
+                   28: "Ar + Ar(4p) => Ar + Ar(m)",
+                   29: "Ar + Ar(4p) => Ar + Ar(r)",
+                   30: "Ar(m) + Ar(4p) => E + Ar+ + Ar",
+                   31: "Ar(r) + Ar(4p) => E + Ar+ + Ar",
+                   32: "StepIonization",
+                   33: "Deexci-2p"}
+
+    # 4) Set values in params class
+    params.D[0]    = De
+    params.D[1]    = Di
+    params.D[2]    = Dm
+    params.D[3]    = Dr
+    params.D[4]    = D4p
+
+    params.mu[0]   = mue
+    params.mu[1]   = mui
+    params.mu[2]   = mum
+    params.mu[3]   = mur
+    params.mu[4]   = mu4p
+
+    params.A[:]    = Ck[:]
+    params.B[:]    = B[:]
+    params.C[:]    = A[:]
+
+    # Account for the 2/3 term to convert from electron temperature to electron energy
+    for i in range(len(params.A)):
+        params.A[i] *= (2/3)**(params.B[i])
+
+    params.dH[:]   = dH[:]
+    params.dEps[:] = dEps[:]
+    params.qStar   = qStar
+    params.alpha   = alpha
+    params.ks      = ks
+    params.gam     = gam
+    params.kappaB  = kappaB
+    params.nAronp0 = nAr / np0
+    params.p0      = p0
+    params.Tg0     = Tg0
+    params.EC      = 2.0 * me / mAr \
+        * np.sqrt(16.0 * (me + mAr) * e0 * c**2
+                  / (3.0 * np.pi * me * mAr)) * se * nAr * tau
+    # params.EC = 2.0 * me / mAr * 3.8e9 * tau
+
+    params.verticalShift = verticalShift / V0
+
+    # Parameters needed to compute the current with dimensions
+    params.V0Ltau  = V0 / (L * tau)
+    params.V0L     = V0 / L
+    params.LLV0tau = (L*L) / (V0*tau)
+    params.tauL    = L / tau
+    params.np0     = np0           # "nominal" electron density [1/m^3]
+    params.qe      = qe            # unit charge [C]
+    params.eps0    = eps0          # unit charge [C]
+    params.eArea   = electrodeArea # electrode area [m^2]
+
+
+    reactionExpressionslist = [f"{params.A[0]} * energy**{params.B[0]} * np.exp(-{params.C[0]} / energy)",
+                               f"{params.A[1]} * energy**{params.B[1]} * np.exp(-{params.C[1]} / energy)",
+                               f"{params.A[2]} * energy**{params.B[2]} * np.exp(-{params.C[2]} / energy)",
+                               f"{params.A[3]} * energy**{params.B[3]} * np.exp(-{params.C[3]} / energy)",
+                               f"{params.A[4]} * energy**{params.B[4]} * np.exp(-{params.C[4]} / energy)",
+                               f"{params.A[5]} * energy**{params.B[5]} * np.exp(-{params.C[5]} / energy)",
+                               f"{params.A[6]} * energy**{params.B[6]} * np.exp(-{params.C[6]} / energy)",
+                               f"{params.A[7]} * energy**{params.B[7]} * np.exp(-{params.C[7]} / energy)",
+                               f"{params.A[8]} * energy**{params.B[8]} * np.exp(-{params.C[8]} / energy)",
+                               f"{params.A[9]} * energy**{params.B[9]} * np.exp(-{params.C[9]} / energy)",
+                               f"{params.A[10]} * energy**{params.B[10]} * np.exp(-{params.C[10]} / energy)",
+                               f"{params.A[11]} * energy**{params.B[11]} * np.exp(-{params.C[11]} / energy)",
+                               f"{params.A[12]} * energy**{params.B[12]} * np.exp(-{params.C[12]} / energy)",
+                               f"{params.A[13]} * energy**{params.B[13]} * np.exp(-{params.C[13]} / energy)",
+                               f"{params.A[14]} * energy**{params.B[14]} * np.exp(-{params.C[14]} / energy)",
+                               f"{params.A[15]} * energy**{params.B[15]} * np.exp(-{params.C[15]} / energy)",
+                               f"{params.A[16]} * energy**{params.B[16]} * np.exp(-{params.C[16]} / energy)",
+                               f"{params.A[17]} * energy**{params.B[17]} * np.exp(-{params.C[17]} / energy)",
+                               f"{params.A[18]} * energy**{params.B[18]} * np.exp(-{params.C[18]} / energy)",
+                               f"{params.A[19]} * energy**{params.B[19]} * np.exp(-{params.C[19]} / energy)",
+                               f"{params.A[20]} * energy**{params.B[20]} * np.exp(-{params.C[20]} / energy)",
+                               f"{params.A[21]} * energy**{params.B[21]} * np.exp(-{params.C[21]} / energy)",
+                               f"{params.A[22]} * energy**{params.B[22]} * np.exp(-{params.C[22]} / energy)",
+                               f"{params.A[23]} * energy**{params.B[23]} * np.exp(-{params.C[23]} / energy)",
+                               f"{params.A[24]} * energy**{params.B[24]} * np.exp(-{params.C[24]} / energy)",
+                               f"{params.A[25]} * energy**{params.B[25]} * np.exp(-{params.C[25]} / energy)",
+                               f"{params.A[26]} * energy**{params.B[26]} * np.exp(-{params.C[26]} / energy)",
+                               f"{params.A[27]} * energy**{params.B[27]} * np.exp(-{params.C[27]} / energy)",
+                               f"{params.A[28]} * energy**{params.B[28]} * np.exp(-{params.C[28]} / energy)",
+                               f"{params.A[29]} * energy**{params.B[29]} * np.exp(-{params.C[29]} / energy)",
+                               f"{params.A[30]} * energy**{params.B[30]} * np.exp(-{params.C[30]} / energy)",
+                               f"{params.A[31]} * energy**{params.B[31]} * np.exp(-{params.C[31]} / energy)",
+                               f"{params.A[32]} * energy**{params.B[32]} * np.exp(-{params.C[32]} / energy)",
+                               f"{params.A[33]} * energy**{params.B[33]} * np.exp(-{params.C[33]} / energy)"]
+
+
+    reactionTExpressionslist = [f"{params.A[0]} * (energy**({params.B[0]}-1)) * np.exp(-{params.C[0]}/energy) * ({params.B[0]} + {params.C[0]}/energy)",
+                                f"{params.A[1]} * (energy**({params.B[1]}-1)) * np.exp(-{params.C[1]}/energy) * ({params.B[1]} + {params.C[1]}/energy)",
+                                f"{params.A[2]} * (energy**({params.B[2]}-1)) * np.exp(-{params.C[2]}/energy) * ({params.B[2]} + {params.C[2]}/energy)",
+                                f"{params.A[3]} * (energy**({params.B[3]}-1)) * np.exp(-{params.C[3]}/energy) * ({params.B[3]} + {params.C[3]}/energy)",
+                                f"{params.A[4]} * (energy**({params.B[4]}-1)) * np.exp(-{params.C[4]}/energy) * ({params.B[4]} + {params.C[4]}/energy)",
+                                f"{params.A[5]} * (energy**({params.B[5]}-1)) * np.exp(-{params.C[5]}/energy) * ({params.B[5]} + {params.C[5]}/energy)",
+                                f"{params.A[6]} * (energy**({params.B[6]}-1)) * np.exp(-{params.C[6]}/energy) * ({params.B[6]} + {params.C[6]}/energy)",
+				f"{params.A[7]} * (energy**({params.B[7]}-1)) * np.exp(-{params.C[7]}/energy) * ({params.B[7]} + {params.C[7]}/energy)",
+		                f"{params.A[8]} * (energy**({params.B[8]}-1)) * np.exp(-{params.C[8]}/energy) * ({params.B[8]} + {params.C[8]}/energy)",
+                                f"{params.A[9]} * (energy**({params.B[9]}-1)) * np.exp(-{params.C[9]}/energy) * ({params.B[9]} + {params.C[9]}/energy)",
+                                f"{params.A[10]} * (energy**({params.B[10]}-1)) * np.exp(-{params.C[10]}/energy) * ({params.B[10]} + {params.C[10]}/energy)",
+                                f"{params.A[11]} * (energy**({params.B[11]}-1)) * np.exp(-{params.C[11]}/energy) * ({params.B[11]} + {params.C[11]}/energy)",
+                                f"{params.A[12]} * (energy**({params.B[12]}-1)) * np.exp(-{params.C[12]}/energy) * ({params.B[12]} + {params.C[12]}/energy)",
+                                f"{params.A[13]} * (energy**({params.B[13]}-1)) * np.exp(-{params.C[13]}/energy) * ({params.B[13]} + {params.C[13]}/energy)",
+                                f"{params.A[14]} * (energy**({params.B[14]}-1)) * np.exp(-{params.C[14]}/energy) * ({params.B[14]} + {params.C[14]}/energy)",
+                                f"{params.A[15]} * (energy**({params.B[15]}-1)) * np.exp(-{params.C[15]}/energy) * ({params.B[15]} + {params.C[15]}/energy)",
+                                f"{params.A[16]} * (energy**({params.B[16]}-1)) * np.exp(-{params.C[16]}/energy) * ({params.B[16]} + {params.C[16]}/energy)",
+                                f"{params.A[17]} * (energy**({params.B[17]}-1)) * np.exp(-{params.C[17]}/energy) * ({params.B[17]} + {params.C[17]}/energy)",
+                                f"{params.A[18]} * (energy**({params.B[18]}-1)) * np.exp(-{params.C[18]}/energy) * ({params.B[18]} + {params.C[18]}/energy)",
+                                f"{params.A[19]} * (energy**({params.B[19]}-1)) * np.exp(-{params.C[19]}/energy) * ({params.B[19]} + {params.C[19]}/energy)",
+                                f"{params.A[20]} * (energy**({params.B[20]}-1)) * np.exp(-{params.C[20]}/energy) * ({params.B[20]} + {params.C[20]}/energy)",
+                                f"{params.A[21]} * (energy**({params.B[21]}-1)) * np.exp(-{params.C[21]}/energy) * ({params.B[21]} + {params.C[21]}/energy)",
+                                f"{params.A[22]} * (energy**({params.B[22]}-1)) * np.exp(-{params.C[22]}/energy) * ({params.B[22]} + {params.C[22]}/energy)",
+                                f"{params.A[23]} * (energy**({params.B[23]}-1)) * np.exp(-{params.C[23]}/energy) * ({params.B[23]} + {params.C[23]}/energy)",
+                                f"{params.A[24]} * (energy**({params.B[24]}-1)) * np.exp(-{params.C[24]}/energy) * ({params.B[24]} + {params.C[24]}/energy)",
+                                f"{params.A[25]} * (energy**({params.B[25]}-1)) * np.exp(-{params.C[25]}/energy) * ({params.B[25]} + {params.C[25]}/energy)",
+                                f"{params.A[26]} * (energy**({params.B[26]}-1)) * np.exp(-{params.C[26]}/energy) * ({params.B[26]} + {params.C[26]}/energy)",
+                                f"{params.A[27]} * (energy**({params.B[27]}-1)) * np.exp(-{params.C[27]}/energy) * ({params.B[27]} + {params.C[27]}/energy)",
+                                f"{params.A[28]} * (energy**({params.B[28]}-1)) * np.exp(-{params.C[28]}/energy) * ({params.B[28]} + {params.C[28]}/energy)",
+                                f"{params.A[29]} * (energy**({params.B[29]}-1)) * np.exp(-{params.C[29]}/energy) * ({params.B[29]} + {params.C[29]}/energy)",
+                                f"{params.A[30]} * (energy**({params.B[30]}-1)) * np.exp(-{params.C[30]}/energy) * ({params.B[30]} + {params.C[30]}/energy)",
+                                f"{params.A[31]} * (energy**({params.B[31]}-1)) * np.exp(-{params.C[31]}/energy) * ({params.B[31]} + {params.C[31]}/energy)",
+                                f"{params.A[32]} * (energy**({params.B[32]}-1)) * np.exp(-{params.C[32]}/energy) * ({params.B[32]} + {params.C[32]}/energy)",
+                                f"{params.A[33]} * (energy**({params.B[33]}-1)) * np.exp(-{params.C[33]}/energy) * ({params.B[33]} + {params.C[33]}/energy)"]
+           
+
+    reactionExpressionTypelist =  np.array([False,False,False,False,False,False,False,False, # Rxns 1-8
+                                           True,True,True,True,True,True,True,True,True,True,True,True,True,True, # Rxns 9-21
+                                           False,False,True,True,True,True,False,False,False,False,True,True]) # Rxns 22-34
+
+    reactionsList = []
+    LOGFilename = 'interpolationSample.log'
+    f = open(LOGFilename, 'w')
+
+    for i in range(Nr):
+        if reactionExpressionTypelist[i]:
+            if i < 14 or i == 16 or i == 19 or i > 23:
+                f = h5.File("../../../BackupRates_Nominal/{0:s}.h5".format(rxnNameDict[i]), 'r')
+                dataset = f["table"]
+            else:
+                f = h5.File("../../../BackupRates_Nominal/StepwiseExcitations.nominal.h5", 'r')
+                dataset = f[rxnNameDict[i]]
+
+            Te = dataset[:,0]
+            Te /= 11604
+            rateCoeff = dataset[:,1]
+            if i > 23 and i < 28:
+                rateCoeff /= 6.022e23**2
+            else:
+                rateCoeff /= 6.022e23
+
+            ## Removing BOLSIG failures
+            fail_inds = []
+            for j in range(len(rateCoeff)):
+                if rateCoeff[j] == 0.0 and j > np.nonzero(rateCoeff)[0][0]:
+                    fail_inds.append(j)
+
+            Te = np.delete(Te, fail_inds)
+            rateCoeff = np.delete(rateCoeff, fail_inds)
+
+            # Sorting mean energy array and rate coefficient array based on
+            # the mean energy array.
+            Teinds = Te.argsort()
+            rateCoeff = rateCoeff[Teinds]
+            Te = Te[Teinds]
+
+            # Find duplicates
+            TeDuplicateinds = np.where(np.abs(np.diff(Te, axis=0)) > 0.0)
+            TeDuplicateindsForLog = np.where(np.abs(np.diff(Te, axis=0)) == 0.0)
+            rateCoeff = rateCoeff[TeDuplicateinds]
+            Te = Te[TeDuplicateinds]
+
+            # Nondimensionalization of mean energy.
+            Te *= 1.5
+
+            # Find first non-zero value of the coefficient rate.
+            I = np.nonzero(rateCoeff)
+
+            diffRateCoeff = [j-i for i, j in zip(rateCoeff[:-1], rateCoeff[1:])]
+            diffTe = [j-i for i, j in zip(Te[:-1], Te[1:])]
+
+            Monotonicity = np.asarray([j/i for i, j in zip(diffTe, diffRateCoeff)])
+            Monotonicity = np.insert(Monotonicity, 0, 0.0, axis=0)
+
+            Nan = np.isnan(Monotonicity)
+            Inf = np.isinf(Monotonicity)
+            indexPositive = np.where(Monotonicity>0.0)
+            Positive = np.full(Monotonicity.shape, False, dtype=bool)
+            Positive[indexPositive] = True
+            
+            indices = Nan + Inf + Positive
+
+
+            #lastFalse = np.where(indices==False)[-1][-1] + 2
+            for k in range(len(Te)):
+               if Te[k] < 4.5 and indices[k] == False:
+                  lastFalse = k + 2
+            #lastFalse = np.nonzero(rateCoeff)[0][0]
+            # Transformation to log scale.
+            TeLog = np.log(Te)
+
+            # Compute the slope of the rate coefficient between its first two non-zero values.
+            # Finite differences are used.
+            dydx = (rateCoeff[lastFalse + 1] - rateCoeff[lastFalse]) \
+                 / (Te[lastFalse + 1] - Te[lastFalse])
+
+            # Arrhenius form: kf = A * exp(-C / Te)
+            C = Te[lastFalse]**2.0*dydx / rateCoeff[lastFalse]
+
+            # Compute pre-exponential coefficient, A, in log scale.
+            ALog = np.log(rateCoeff[lastFalse]) + C / Te[lastFalse]
+
+            # Transform rate coefficient in log scale.
+            rateCoeffLog = np.zeros(rateCoeff.shape)
+            rateCoeffLog[lastFalse:] = np.log(rateCoeff[lastFalse:])
+            # For the troublesome values, we use the Arrhenius form.
+            rateCoeffLog[0:lastFalse] = ALog - C / Te[0:lastFalse]
+            # Nondimensionalization in log scale.
+            if (i < 12):
+                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
+            elif i > 23 and i < 28:
+                rateCoeffLog += - np.log(1.0/tau) + 2*np.log(np0)
+            else:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(np0)
+
+            # Interpolation in log scale.
+            reactionExpressionsLog = CubicSpline(TeLog, rateCoeffLog)
+            # Gradient in log scale
+            reactionTExpressionsLog = CubicSpline.derivative(reactionExpressionsLog)
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf_log = reactionExpressionsLog,
+                                kf_T_log = reactionTExpressionsLog)
+            reactionsList.append(reaction)
+
+            logging.basicConfig(filename=LOGFilename)
+            logging.warning('Interpolation info (Reaction %s):', i + 1)
+            logging.warning('First non-zero entry in the rate coefficient: %s', I[0][0])
+            logging.warning('Monotonicity of the rate coefficient start from entry: %s', lastFalse)
+            logging.warning('Position of possible duplicates in mean energy array: %s',
+                          TeDuplicateindsForLog[0])
+
+        else:
+            rxn   = eval("lambda energy :" + reactionExpressionslist[i])
+            rxn_T = eval("lambda energy :" + reactionTExpressionslist[i])
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf = rxn, kf_T = rxn_T)
+            reactionsList.append(reaction)
+
+    params.reactionsList = reactionsList
+
+    ## Electron Transport Data
+    diffList = []
+    transport = h5.File("../../../BOLSIGChemistry_Transport/nominal_transport.h5", 'r')
+    NDe_v_Te = transport["diffusivity"]
+    Te_trans = NDe_v_Te[:,0]
+    Te_trans /= 11604
+    De_interp = (NDe_v_Te[:,1]/nAr)*tau/(L*L)
+    De_spline = CubicSpline(Te_trans, De_interp)
+    De_Te_spline = CubicSpline.derivative(De_spline)
+    diffusivity = Diffusivity(interpolate = True, D_expression = De_spline, D_T_expression = De_Te_spline)
+    diffList.append(diffusivity)
+
+    Ns = 6
+    for i in range(1, Ns):
+        diffList.append(Diffusivity(interpolate = False))
+
+    params.diffusivityList = diffList
+
+    muList = []
+    Nmue_v_Te = transport["mobility"]
+    mue_interp = (Nmue_v_Te[:,1]/nAr)*V0*tau/(L*L)
+    mue_spline = CubicSpline(Te_trans, mue_interp)
+    mue_Te_spline = CubicSpline.derivative(mue_spline)
+    mobility = Mobility(interpolate = True, mu_expression = mue_spline, mu_T_expression = mue_Te_spline)
+    muList.append(mobility)
+
+    Ns = 6
+    for i in range(1, Ns):
+        muList.append(Mobility(interpolate = False))
+
+    params.mobilityList = muList
+    
+    # 5) Dump to screen
+    params.print()
diff --git a/psaapProperties_6Species_250mTorr.py b/psaapProperties_6Species_250mTorr.py
new file mode 100755
index 000000000..466192108
--- /dev/null
+++ b/psaapProperties_6Species_250mTorr.py
@@ -0,0 +1,533 @@
+import numpy as np
+from scipy.interpolate import CubicSpline
+import csv
+#import matplotlib.pyplot as plt
+#import matplotlib.colors as mcolors
+import h5py as h5
+
+import logging
+
+class Reaction(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Diffusivity(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Mobility(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+
+def setPsaapProperties_6Species_250mTorr(gam, inputV0, inputVDC, params, Nr, iSample):
+    """Sets non-dimensional properties corresponding to Liu 2014 paper.
+
+    Inputs:
+      gam : Secondary electron emission coefficient
+      params : chebSolver.modelParams class
+
+    Outputs: None
+      params data is overwritten using values from Liu 2014.
+    """
+    ###################################################################
+    # User specified parameters (you may change these if you wish to
+    # run a different scenario from Liu 2014)
+    ###################################################################
+
+    # densities
+    nAr = 8.05e21     # background number density of Ar [1/m^3] (corresponds to p=100 mTorr)
+    np0 = 8e16        # "nominal" electron density [1/m^3]
+
+    # masses
+    # me = 9.10938356e-31        # mass of an electron [kg]
+    # me = 5.489e-4              # mass of an electron [u]
+    me = 0.511e6                 # mass of an electron [eV/c2]
+    # mAr = 39.948               # mass of an argon atom [u]
+    # mAr = 39.948 * 1.66054e-27 # mass of an argon atom [kg]
+    mAr = 37.2158e9              # mass of an argon atom [eV/c2]
+    # u = 931.4941e6             # eV/c2
+    c = 299792458                # speed of light [m/s]
+    se = 40                      # momentum cross section [A^2]
+
+    # nominal electron energy
+    e0 = 1.0  # [eV]
+
+    # pressure
+    p  = 33.33*1.5      # [J/m^3] *1.5 to convert it to energy (1 Torr)
+
+    # gas energy at the wall
+    Tg0 = 0.038778    # 3/2*300K*kB ~ (p0 - nT[:,0])/ntot
+
+    # characteristics of driving voltage
+    V0  = inputV0                 # amplitude of driving voltage [V]
+    verticalShift = inputVDC      # DC voltage (vertical shift in driving voltage)
+    tau = (1./13.56e6)             # period of driving voltage [s]
+    L   = 2.00*0.005              # half-gap-width [m] (gap width is 2 cm)
+    electrodeArea = np.pi*0.05**2 # electrode area [m^2] (electrode diameter = 0.1 m)
+
+    # transport parameters
+    nmue = 9.66e21   # argon number density times electron mobility [1/(V*cm*s)]
+    nmum = 0.0
+    nmur = 0.0
+    nmu4p = 0.0
+    nmui = 8.0e19
+    nDe  = 3.86e22   # argon number density times electron diffusivity [1/(cm*s)]
+    nDi  = 2.07e18   # argon number density times ion diffusivity [1/(cm*s)]
+    nDm  = 2.42e18   # argon number density times AR(m) diffusivity [1/(cm*s)]
+    nDr  = 2.42e18
+    nD4p = 2.42e18
+
+    # reaction parameters (NB: k_i = Ck*Ee^B*exp(-A/Ee))
+    #                          Ee = 3/2*Te (Te in eV)
+    #                          -> k_i = [Ck*(2/3)^B] * Ee^B * exp[-(3/2)*A/Ee]
+    # nominal
+    Ck = np.array([2.0e-7,2.1e-9,5.0e-10,6.4e-10,2.1e-15,1.32e8,0.0,3.0e7,3.0e7,0.0,0.0,0.0,0.0,4.3e-10,0.0,3.7e-8,8.9e-7,1.8e-7,3.0e-7,3.0e-7,4.3e-10,9.1e-7,8.9e-7]) # pre-exponential factors [cm^3/s]
+    B  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0.74,0,0,0.51,0.61,0.51,0.51,0.74,0,0.51])
+    A  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.59,2.61,0,0,0,0,1.59]) # activation temperature [eV]
+    dH = np.array([0.0,-7.412,-10.054,-7.336,0.0,0.0,0.0,0.0,0.0,11.548,11.624,12.907,15.76,-11.548,4.212,0.076,1.359,2.853,-1.283,-1.359,-11.624,-0.076,0.983]) # energy lost per electron due to ionization rxn [eV]
+    dEps = np.array([0.0,15.76,11.548,11.624,12.907,0.0]) # E, AR+, AR(m), AR(r), AR(4p), AR
+
+    # BC parameters
+    # ks = 1.19e7  # electron recombination rate [cm/s]
+    ks = 1.366109824889323e7 # electron recombination rate [cm/s/eV]
+
+    ###################################################################
+    # Constants of nature (probably shouldn't change unless you have
+    # root privileges on universe)
+    ###################################################################
+    qe   = 1.6e-19   # unit charge [C]
+    eps0 = 8.86e-12  # permittivity of free space [F/m]
+    kB   = 1.38e-23  # Boltzmann constant [J/K]
+    # kB   = 8.62e-5 # Boltzmann constant [eV/K]
+
+
+    ###################################################################
+    # Calculate non-dimensional parameters
+    ###################################################################
+
+    # 1) Convert input units to base SI (except eV)
+    nDe  *= 100. # 1/(m*s)
+    nDi  *= 100. # 1/(m*s)
+    nDm  *= 100.
+    nDr  *= 100.
+    nD4p *= 100.
+    nmue *= 100. # 1/(V*m*s)
+    nmui *= 100. # 1/(V*m*s)
+    Ck[0:5] *= 1e-6 # m^3/s
+    Ck[5:9] *= 1 # 1/s
+    Ck[9:]  *= 1e-6 # m^3/s
+    ks   *= 0.01 # m/s
+    se   *= 1.0e-20  # m^2
+
+    # 2) Compute "raw" transport parameters
+    De  = nDe/nAr
+    Di  = nDi/nAr
+    Dm  = nDm/nAr
+    Dr  = nDr/nAr
+    D4p = nD4p/nAr
+
+    mue = nmue/nAr
+    mui = nmui/nAr
+    mum = nmum/nAr
+    mur = nmur/nAr
+    mu4p = nmu4p/nAr
+
+    # 3) Compute non-dimensional properties required by solver
+    De    = De*tau/(L*L)
+    Di    = Di*tau/(L*L)
+    Dm    = Dm*tau/(L*L)
+    Dr    = Dr*tau/(L*L)
+    D4p   = D4p*tau/(L*L)
+
+    mue   = mue*V0*tau/(L*L)
+    mui   = mui*V0*tau/(L*L)
+    mum   = mum*V0*tau/(L*L)
+    mur   = mur*V0*tau/(L*L)
+    mu4p  = mu4p*V0*tau/(L*L)
+
+    Ck[0:4]  *= tau*np0
+    Ck[4]    *= tau*nAr
+    Ck[5:9]  *= tau
+    Ck[9:13] *= tau*nAr
+    Ck[13:]  *= tau*np0
+    A        = A*1.5/e0  # 1.5 to convert from temperature to energy
+    dH       = dH/e0
+    qStar    = V0/e0 # qe*V0/e0, since e0 in eV, need qe*V0 in eV, which is just V0 in V
+    alpha    = qe*np0*L*L/(V0*eps0)
+    ks       = ks*tau/L
+    p0       = p/qe/np0
+    kappaB   = 4.878171165833662*1.6129 # non-dimensional thermal conductivity of background specie
+                                # (2/3)*tau/L**2*Kb/np0/kB,
+                                # where Kb is the thermal conductivity of background specie
+
+    params.beta = np.array([[0,1,1,1,0,0,0,0,0,1,1,1,2,1,2,1,1,2,1,1,1,1,1],                     # E
+                            [0,1,1,1,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0],                     # AR+
+                            [0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0],                     # AR(m)
+                            [0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0],                     # AR(r)
+                            [0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1],                     # AR(4p)
+                            [2,1,1,1,2,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0]], dtype=np.int64)    # AR
+
+    params.alfa = np.array([[0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1],                     # E
+                            [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],                     # AR+
+                            [2,1,0,2,1,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0],                     # AR(m)
+                            [0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1],                     # AR(r)
+                            [0,0,2,0,0,0,1,1,1,0,0,0,0,0,0,0,0,1,1,1,0,0,0],                     # AR(4p)
+                            [0,0,0,0,1,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0]], dtype=np.int64)    # AR
+	# Rxn1:  2AR(m)         ->   2AR
+	# Rxn2:  AR(m) + AR(r)  ->   E + AR+ + AR
+	# Rxn3:  2AR(4p)        ->   E + AR+ + AR
+	# Rxn4:  2AR(m)         ->   E + AR+ + AR
+	# Rxn5:  AR(m) + AR     ->   2AR
+	# Rxn6:  AR(r)          ->   AR
+	# Rxn7:  AR(4p)         ->   AR
+	# Rxn8:  AR(4p)         ->   AR(m)
+	# Rxn9:  AR(4p)         ->   AR(r)
+    # Rxn10: E + AR         ->   E + AR(m)
+    # Rxn11: E + AR         ->   E + AR(r)
+    # Rxn12: E + AR         ->   E + AR(4p)
+    # Rxn13: E + AR         ->   2E + AR+
+    # Rxn14: E + AR(m)      ->   E + AR
+    # Rxn15: E + AR(m)      ->   2E + AR+
+    # Rxn16: E + AR(m)      ->   E + AR(r)
+    # Rxn17: E + AR(m)      ->   E + AR(4p)
+    # Rxn18: E + AR(4p)     ->   2E + AR+
+    # Rxn19: E + AR(4p)     ->   E + AR(r)
+    # Rxn20: E + AR(4p)     ->   E + AR(m)
+    # Rxn21: E + AR(r)      ->   E + AR
+    # Rxn22: E + AR(r)      ->   E + AR(m)
+    # Rxn23: E + AR(r)      ->   E + AR(4p)
+
+    rxnNameDict = { 0: "2Ar(m) => 2Ar",
+                    1: "Ar(m) + Ar(r) => E + Ar+ + Ar",
+                    2: "2Ar(4p) => E + Ar+ + Ar",
+                    3: "2Ar(m) => E + Ar+ + Ar",
+                    4: "Ar(m) + Ar => 2Ar",
+                    5: "Ar(r) => Ar",
+                    6: "Ar(4p) => Ar",
+                    7: "Ar(4p) => Ar(m)",
+                    8: "Ar(4p) => Ar(r)",
+                    9: "lumped.metastable",
+                   10: "lumped.resonance",
+                   11: "lumped.2p",
+                   12: "ionization",
+                   13: "E + Ar(m) => E + Ar",
+                   14: "step_ionization",
+                   15: "E + Ar(m) => E + Ar(r)",
+                   16: "E + Ar(m) => E + Ar(4p)",
+                   17: "E + Ar(4p) => 2E + Ar+",
+                   18: "E + Ar(4p) => E + Ar(r)",
+                   19: "E + Ar(4p) => E + Ar(m)",
+                   20: "E + Ar(r) => E + Ar",
+                   21: "E + Ar(r) => E + Ar(m)",
+                   22: "E + Ar(r) => E + Ar(4p)"}
+
+    # 4) Set values in params class
+    params.D[0]    = De
+    params.D[1]    = Di
+    params.D[2]    = Dm
+    params.D[3]    = Dr
+    params.D[4]    = D4p
+
+    params.mu[0]   = mue
+    params.mu[1]   = mui
+    params.mu[2]   = mum
+    params.mu[3]   = mur
+    params.mu[4]   = mu4p
+
+    params.A[:]    = Ck[:]
+    params.B[:]    = B[:]
+    params.C[:]    = A[:]
+
+    # Account for the 2/3 term to convert from electron temperature to electron energy
+    for i in range(len(params.A)):
+        params.A[i] *= (2/3)**(params.B[i])
+
+    params.dH[:]   = dH[:]
+    params.dEps[:] = dEps[:]
+    params.qStar   = qStar
+    params.alpha   = alpha
+    params.ks      = ks
+    params.gam     = gam
+    params.kappaB  = kappaB
+    params.nAronp0 = nAr / np0
+    params.p0      = p0
+    params.Tg0     = Tg0
+    params.EC      = 2.0 * me / mAr \
+        * np.sqrt(16.0 * (me + mAr) * e0 * c**2
+                  / (3.0 * np.pi * me * mAr)) * se * nAr * tau
+    # params.EC = 2.0 * me / mAr * 3.8e9 * tau
+
+    params.verticalShift = verticalShift / V0
+
+    # Parameters needed to compute the current with dimensions
+    params.V0Ltau  = V0 / (L * tau)
+    params.V0L     = V0 / L
+    params.LLV0tau = (L*L) / (V0*tau)
+    params.tauL    = L / tau
+    params.np0     = np0           # "nominal" electron density [1/m^3]
+    params.qe      = qe            # unit charge [C]
+    params.eps0    = eps0          # unit charge [C]
+    params.eArea   = electrodeArea # electrode area [m^2]
+
+
+    reactionExpressionslist = [f"{params.A[0]} * energy**{params.B[0]} * np.exp(-{params.C[0]} / energy)",
+                               f"{params.A[1]} * energy**{params.B[1]} * np.exp(-{params.C[1]} / energy)",
+                               f"{params.A[2]} * energy**{params.B[2]} * np.exp(-{params.C[2]} / energy)",
+                               f"{params.A[3]} * energy**{params.B[3]} * np.exp(-{params.C[3]} / energy)",
+                               f"{params.A[4]} * energy**{params.B[4]} * np.exp(-{params.C[4]} / energy)",
+                               f"{params.A[5]} * energy**{params.B[5]} * np.exp(-{params.C[5]} / energy)",
+                               f"{params.A[6]} * energy**{params.B[6]} * np.exp(-{params.C[6]} / energy)",
+                               f"{params.A[7]} * energy**{params.B[7]} * np.exp(-{params.C[7]} / energy)",
+                               f"{params.A[8]} * energy**{params.B[8]} * np.exp(-{params.C[8]} / energy)",
+                               f"{params.A[9]} * energy**{params.B[9]} * np.exp(-{params.C[9]} / energy)",
+                               f"{params.A[10]} * energy**{params.B[10]} * np.exp(-{params.C[10]} / energy)",
+                               f"{params.A[11]} * energy**{params.B[11]} * np.exp(-{params.C[11]} / energy)",
+                               f"{params.A[12]} * energy**{params.B[12]} * np.exp(-{params.C[12]} / energy)",
+                               f"{params.A[13]} * energy**{params.B[13]} * np.exp(-{params.C[13]} / energy)",
+                               f"{params.A[14]} * energy**{params.B[14]} * np.exp(-{params.C[14]} / energy)",
+                               f"{params.A[15]} * energy**{params.B[15]} * np.exp(-{params.C[15]} / energy)",
+                               f"{params.A[16]} * energy**{params.B[16]} * np.exp(-{params.C[16]} / energy)",
+                               f"{params.A[17]} * energy**{params.B[17]} * np.exp(-{params.C[17]} / energy)",
+                               f"{params.A[18]} * energy**{params.B[18]} * np.exp(-{params.C[18]} / energy)",
+                               f"{params.A[19]} * energy**{params.B[19]} * np.exp(-{params.C[19]} / energy)",
+                               f"{params.A[20]} * energy**{params.B[20]} * np.exp(-{params.C[20]} / energy)",
+                               f"{params.A[21]} * energy**{params.B[21]} * np.exp(-{params.C[21]} / energy)",
+                               f"{params.A[22]} * energy**{params.B[22]} * np.exp(-{params.C[22]} / energy)"]
+
+    reactionTExpressionslist = [f"{params.A[0]} * (energy**({params.B[0]}-1)) * np.exp(-{params.C[0]}/energy) * ({params.B[0]} + {params.C[0]}/energy)",
+                                f"{params.A[1]} * (energy**({params.B[1]}-1)) * np.exp(-{params.C[1]}/energy) * ({params.B[1]} + {params.C[1]}/energy)",
+                                f"{params.A[2]} * (energy**({params.B[2]}-1)) * np.exp(-{params.C[2]}/energy) * ({params.B[2]} + {params.C[2]}/energy)",
+                                f"{params.A[3]} * (energy**({params.B[3]}-1)) * np.exp(-{params.C[3]}/energy) * ({params.B[3]} + {params.C[3]}/energy)",
+                                f"{params.A[4]} * (energy**({params.B[4]}-1)) * np.exp(-{params.C[4]}/energy) * ({params.B[4]} + {params.C[4]}/energy)",
+                                f"{params.A[5]} * (energy**({params.B[5]}-1)) * np.exp(-{params.C[5]}/energy) * ({params.B[5]} + {params.C[5]}/energy)",
+                                f"{params.A[6]} * (energy**({params.B[6]}-1)) * np.exp(-{params.C[6]}/energy) * ({params.B[6]} + {params.C[6]}/energy)",
+				f"{params.A[7]} * (energy**({params.B[7]}-1)) * np.exp(-{params.C[7]}/energy) * ({params.B[7]} + {params.C[7]}/energy)",
+		                f"{params.A[8]} * (energy**({params.B[8]}-1)) * np.exp(-{params.C[8]}/energy) * ({params.B[8]} + {params.C[8]}/energy)",
+                                f"{params.A[9]} * (energy**({params.B[9]}-1)) * np.exp(-{params.C[9]}/energy) * ({params.B[9]} + {params.C[9]}/energy)",
+                                f"{params.A[10]} * (energy**({params.B[10]}-1)) * np.exp(-{params.C[10]}/energy) * ({params.B[10]} + {params.C[10]}/energy)",
+                                f"{params.A[11]} * (energy**({params.B[11]}-1)) * np.exp(-{params.C[11]}/energy) * ({params.B[11]} + {params.C[11]}/energy)",
+                                f"{params.A[12]} * (energy**({params.B[12]}-1)) * np.exp(-{params.C[12]}/energy) * ({params.B[12]} + {params.C[12]}/energy)",
+                                f"{params.A[13]} * (energy**({params.B[13]}-1)) * np.exp(-{params.C[13]}/energy) * ({params.B[13]} + {params.C[13]}/energy)",
+                                f"{params.A[14]} * (energy**({params.B[14]}-1)) * np.exp(-{params.C[14]}/energy) * ({params.B[14]} + {params.C[14]}/energy)",
+                                f"{params.A[15]} * (energy**({params.B[15]}-1)) * np.exp(-{params.C[15]}/energy) * ({params.B[15]} + {params.C[15]}/energy)",
+                                f"{params.A[16]} * (energy**({params.B[16]}-1)) * np.exp(-{params.C[16]}/energy) * ({params.B[16]} + {params.C[16]}/energy)",
+                                f"{params.A[17]} * (energy**({params.B[17]}-1)) * np.exp(-{params.C[17]}/energy) * ({params.B[17]} + {params.C[17]}/energy)",
+                                f"{params.A[18]} * (energy**({params.B[18]}-1)) * np.exp(-{params.C[18]}/energy) * ({params.B[18]} + {params.C[18]}/energy)",
+                                f"{params.A[19]} * (energy**({params.B[19]}-1)) * np.exp(-{params.C[19]}/energy) * ({params.B[19]} + {params.C[19]}/energy)",
+                                f"{params.A[20]} * (energy**({params.B[20]}-1)) * np.exp(-{params.C[20]}/energy) * ({params.B[20]} + {params.C[20]}/energy)",
+                                f"{params.A[21]} * (energy**({params.B[21]}-1)) * np.exp(-{params.C[21]}/energy) * ({params.B[21]} + {params.C[21]}/energy)",
+                                f"{params.A[22]} * (energy**({params.B[22]}-1)) * np.exp(-{params.C[22]}/energy) * ({params.B[22]} + {params.C[22]}/energy)"]
+           
+
+    reactionExpressionTypelist =  np.array([False,False,False,False,False,False,False,False,False,
+                                           True,True,True,True,False,True,True,True,False,True,True,False,True,True])
+
+    reactionsList = []
+    LOGFilename = 'interpolationSample.log'
+    f = open(LOGFilename, 'w')
+
+    for i in range(Nr):
+        if reactionExpressionTypelist[i]:
+            if (i < 15):
+                f = h5.File("../BOLSIGChemistry_6SpeciesRates/{0:s}.h5".format(rxnNameDict[i]), 'r')
+                dataset = f["table"]
+            else:
+                f = h5.File("../BOLSIGChemistry_6SpeciesRates/StepwiseExcitations.nominal.h5", 'r')
+                dataset = f[rxnNameDict[i]]
+
+            Te = dataset[:,0]
+            Te /= 11604
+            rateCoeff = dataset[:,1]
+            rateCoeff /= 6.022e23
+
+            # Sorting mean energy array and rate coefficient array based on
+            # the mean energy array.
+            Teinds = Te.argsort()
+            rateCoeff = rateCoeff[Teinds]
+            Te = Te[Teinds]
+
+            # Find duplicates
+            TeDuplicateinds = np.where(np.abs(np.diff(Te, axis=0)) > 0.0)
+            TeDuplicateindsForLog = np.where(np.abs(np.diff(Te, axis=0)) == 0.0)
+            rateCoeff = rateCoeff[TeDuplicateinds]
+            Te = Te[TeDuplicateinds]
+
+            # Nondimensionalization of mean energy.
+            Te *= 1.5
+
+            # Find first non-zero value of the coefficient rate.
+            I = np.nonzero(rateCoeff)
+
+            diffRateCoeff = [j-i for i, j in zip(rateCoeff[:-1], rateCoeff[1:])]
+            diffTe = [j-i for i, j in zip(Te[:-1], Te[1:])]
+
+            Monotonicity = np.asarray([j/i for i, j in zip(diffTe, diffRateCoeff)])
+            Monotonicity = np.insert(Monotonicity, 0, 0.0, axis=0)
+
+            Nan = np.isnan(Monotonicity)
+            Inf = np.isinf(Monotonicity)
+            indexPositive = np.where(Monotonicity>0.0)
+            Positive = np.full(Monotonicity.shape, False, dtype=bool)
+            Positive[indexPositive] = True
+            
+            indices = Nan + Inf + Positive
+
+
+            #lastFalse = np.where(indices==False)[-1][-1] + 2
+            for k in range(len(Te)):
+               if Te[k] < 4.5 and indices[k] == False:
+                  lastFalse = k + 2
+
+            # Transformation to log scale.
+            TeLog = np.log(Te)
+
+            # Compute the slope of the rate coefficient between its first two non-zero values.
+            # Finite differences are used.
+            dydx = (rateCoeff[lastFalse + 1] - rateCoeff[lastFalse]) \
+                 / (Te[lastFalse + 1] - Te[lastFalse])
+
+            # Arrhenius form: kf = A * exp(-C / Te)
+            C = Te[lastFalse]**2.0*dydx / rateCoeff[lastFalse]
+
+            # Compute pre-exponential coefficient, A, in log scale.
+            ALog = np.log(rateCoeff[lastFalse]) + C / Te[lastFalse]
+
+            # Transform rate coefficient in log scale.
+            rateCoeffLog = np.zeros(rateCoeff.shape)
+            rateCoeffLog[lastFalse:] = np.log(rateCoeff[lastFalse:])
+            # For the troublesome values, we use the Arrhenius form.
+            rateCoeffLog[0:lastFalse] = ALog - C / Te[0:lastFalse]
+            # Nondimensionalization in log scale.
+            if (i < 13):
+                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
+            else:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(np0)
+
+            # Interpolation in log scale.
+            reactionExpressionsLog = CubicSpline(TeLog, rateCoeffLog)
+            # Gradient in log scale
+            reactionTExpressionsLog = CubicSpline.derivative(reactionExpressionsLog)
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf_log = reactionExpressionsLog,
+                                kf_T_log = reactionTExpressionsLog)
+            reactionsList.append(reaction)
+
+            logging.basicConfig(filename=LOGFilename)
+            logging.warning('Interpolation info (Reaction %s):', i + 1)
+            logging.warning('First non-zero entry in the rate coefficient: %s', I[0][0])
+            logging.warning('Monotonicity of the rate coefficient start from entry: %s', lastFalse)
+            logging.warning('Position of possible duplicates in mean energy array: %s',
+                          TeDuplicateindsForLog[0])
+
+        else:
+            rxn   = eval("lambda energy :" + reactionExpressionslist[i])
+            rxn_T = eval("lambda energy :" + reactionTExpressionslist[i])
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf = rxn, kf_T = rxn_T)
+            reactionsList.append(reaction)
+
+    params.reactionsList = reactionsList
+
+    diffList = []
+    # Data from BOLSIG
+    # Te in [eV]
+    # De * N in [1/(m*s)]
+    #NDe_v_Te = np.array([ [-0.05, 1.8e25],         [0.0, 1.8e25],           [0.05, 1.8e25],          [0.1, 1.8e25],
+    #                      [0.2103718, 1.8e25],     [0.2244455, 1.8e25],     [0.2390528, 1.8e25],     [0.2544605, 1.8e25],
+    #                      [0.2710021, 1.8e25],     [0.2886776, 1.8e25],     [0.3078205, 1.8e25],     [0.3286309, 1.8e25],
+    #                      [0.3513089, 1.8e25],     [0.3760546, 1.8e25],     [0.4032682, 1.8e25],     [0.4331498, 1.8e25],
+    #                      [0.4659662, 1.8e25],     [0.5021843, 1.8e25],     [0.5424044, 1.8e25],     [0.5868266, 1.8e25],
+    #                      [0.6359845, 1.8e25],     [0.690345, 1.8e25],      [0.749041, 1.8e25],      [0.813073, 1.8e25],
+    #                      [1.217942, 1.46E+25],    [1.319326, 1.39E+25],    [1.430048, 1.32E+25],    [1.550108, 1.26E+25],
+    #                      [1.681507, 1.20E+25],    [1.823578, 1.15E+25],    [1.977655, 1.09E+25],    [2.143071, 1.05E+25],
+    #                      [2.319826, 9.98E+24],    [2.508587, 9.54E+24],    [2.709354, 9.13E+24],    [2.916124, 8.75E+24],
+    #                      [3.112889, 8.45E+24],    [3.272969, 8.24E+24],    [3.383691, 8.14E+24],    [3.456394, 8.09E+24],
+    #                      [3.505085, 8.07E+24],    [3.544438, 8.05E+24],    [3.579789, 8.03E+24],    [3.616474, 8.01E+24],
+    #                      [3.656494, 7.97E+24],    [3.701183, 7.93E+24],    [3.751875, 7.87E+24],    [3.807903, 7.81E+24],
+    #                      [3.871268, 7.75E+24],    [3.941303, 7.68E+24],    [4.018675, 7.61E+24],    [4.102717, 7.53E+24],
+    #                      [4.193429, 7.5E+24],     [4.290811, 7.5E+24],     [4.39553, 7.5E+24],      [4.507586, 7.5E+24],
+    #                      [5., 7.5E+24],           [6., 7.5E+24],           [7., 7.5E+24],           [8., 7.5E+24],
+    #                      [9., 7.5E+24],           [10., 7.5E+24],          [11., 7.5E+24],          [12., 7.5E+24]])
+
+    transport = h5.File("../BOLSIGChemistry_Transport/nominal_transport.h5", 'r')
+    NDe_v_Te = transport["diffusivity"]
+    Te_trans = NDe_v_Te[:,0]
+    Te_trans /= 11604
+    print("Te_min = {0:.6e}".format(NDe_v_Te[0,0]))
+    print("Te_max = {0:.6e}".format(NDe_v_Te[-1,0]))
+    De_interp = (NDe_v_Te[:,1]/nAr)*tau/(L*L)
+    De_spline = CubicSpline(Te_trans, De_interp)
+    De_Te_spline = CubicSpline.derivative(De_spline)
+    diffusivity = Diffusivity(interpolate = True, D_expression = De_spline, D_T_expression = De_Te_spline)
+    diffList.append(diffusivity)
+
+    Ns = 6
+    for i in range(1, Ns):
+        diffList.append(Diffusivity(interpolate = False))
+
+    params.diffusivityList = diffList
+
+    muList = []
+    # Data from BOLSIG
+    # Te in [eV]
+    # Mue * N in [1/(V*m*s)]
+    #Nmue_v_Te = np.array([[0.02846756, 1.289e+26], [0.02975487, 1.33e+26],   [0.03173586, 1.383e+26],  [0.03477738,1.448e+26],
+    #                      [0.03937968, 1.523e+26], [0.04614973, 1.604e+26],  [0.05561446, 1.679e+26],  [0.0681674,1.735e+26],
+    #                      [0.0835084, 1.749e+26],  [0.1008504, 1.718e+26],   [0.1187927, 1.639e+26],   [0.1363348,1.522e+26],
+    #                      [0.1528764, 1.386e+26],  [0.1682841, 1.245e+26],   [0.1826913, 1.108e+26],   [0.1965649,9.809e+25],
+    #                      [0.2103718, 8.662e+25],  [0.2244455, 7.641e+25],   [0.2390528, 6.74e+25],    [0.2544605,5.947e+25],
+    #                      [0.2710021, 5.249e+25],  [0.2886776, 4.636e+25],   [0.3078205, 4.097e+25],   [0.3286309,3.623e+25],
+    #                      [0.3513089, 3.206e+25],  [0.3760546, 2.839e+25],   [0.4032682, 2.517e+25],   [0.4331498,2.232e+25],
+    #                      [0.4659662, 1.981e+25],  [0.5021843, 1.76e+25],    [0.5424044, 1.565e+25],   [0.5868266,1.392e+25],
+    #                      [0.6359845, 1.239e+25],  [0.690345, 1.103e+25],    [0.749041,  9.8e+24],     [0.813073,8.699e+24],
+    #                      [0.882441, 7.711e+24],   [0.957145, 6.828e+24],    [1.037185,  6.04e+24],    [1.123895,5.344e+24],
+    #                      [1.217942, 4.729e+24],   [1.319326, 4.186e+24],    [1.430048,  3.707e+24],   [1.550108,3.283e+24],
+    #                      [1.681507, 2.907e+24],   [1.823578, 2.574e+24],    [1.977655,  2.278e+24],   [2.143071,2.015e+24],
+    #                      [2.319826, 1.779e+24],   [2.508587, 1.569e+24],    [2.709354,  1.384e+24],   [2.916124,1.228e+24],
+    #                      [3.112889, 1.115e+24],   [3.272969, 1.055e+24],    [3.383691,  1.038e+24],   [3.456394,1.044e+24],
+    #                      [3.505085, 1.054e+24],   [3.544438, 1.062e+24],    [3.579789,  1.064e+24],   [3.616474, 1.059e+24],
+    #                      [3.656494, 1.048e+24],   [3.701183, 1.033e+24],    [3.751875,  1.014e+24],   [3.807903, 9.928e+23],
+    #                      [3.871268, 9.697e+23],   [3.941303,  9.459e+23],   [4.018675, 9.22e+23],     [4.102717, 8.991e+23],
+    #                      [4.193429, 8.773e+23],   [4.290811,  8.57e+23],    [4.39553, 8.383e+23],     [4.507586, 8.21e+23],
+    #                      [4.627646, 8.051e+23],   [4.757711,  7.901e+23],   [4.899115, 7.757e+23],    [5.054526, 7.617e+23],
+    #                      [5.227946, 7.479e+23],   [5.423377,  7.339e+23],   [5.646822, 7.198e+23],    [5.905618, 7.056e+23],
+    #                      [6.20977, 6.91e+23],     [6.571284,  6.757e+23],   [7.01017, 6.607e+23],     [7.54377, 6.458e+23],
+    #                      [8.19743, 6.303e+23],    [9.01117,   6.155e+23],   [10.02501, 6e+23],        [11.30565, 5.843e+23],
+    #                      [12.89978, 5.677e+23],   [14.91412,  5.51e+23],    [17.41537, 5.326e+23],    [20.57028, 5.149e+23],
+    #                      [24.51225, 4.968e+23],   [29.45472,  4.784e+23],   [35.6845, 4.602e+23],     [43.58845, 4.421e+23],
+    #                      [53.86692, 4.269e+23],   [67.367,    4.134e+23],   [85.4427, 4.026e+23],     [100.0, 4.026e+23]])
+
+    Nmue_v_Te = transport["mobility"]
+    #Te = Nmue_v_Te[:,0]
+    #Te /= 11604
+    mue_interp = (Nmue_v_Te[:,1]/nAr)*V0*tau/(L*L)
+    mue_spline = CubicSpline(Te_trans, mue_interp)
+    mue_Te_spline = CubicSpline.derivative(mue_spline)
+    mobility = Mobility(interpolate = True, mu_expression = mue_spline, mu_T_expression = mue_Te_spline)
+    muList.append(mobility)
+
+    Ns = 6
+    for i in range(1, Ns):
+        muList.append(Mobility(interpolate = False))
+
+    params.mobilityList = muList
+    
+    # 5) Dump to screen
+    params.print()
diff --git a/psaapProperties_6Species_250mTorr_Expanded.py b/psaapProperties_6Species_250mTorr_Expanded.py
new file mode 100755
index 000000000..6a0f13a40
--- /dev/null
+++ b/psaapProperties_6Species_250mTorr_Expanded.py
@@ -0,0 +1,547 @@
+import numpy as np
+from scipy.interpolate import CubicSpline
+import csv
+import h5py as h5
+
+import logging
+
+class Reaction(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Diffusivity(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Mobility(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+
+def setPsaapProperties_6Species_250mTorr_Expanded(gam, inputV0, inputVDC, params, Nr, iSample):
+    """Sets non-dimensional properties corresponding to Liu 2014 paper.
+
+    Inputs:
+      gam : Secondary electron emission coefficient
+      params : chebSolver.modelParams class
+
+    Outputs: None
+      params data is overwritten using values from Liu 2014.
+    """
+    ###################################################################
+    # User specified parameters (you may change these if you wish to
+    # run a different scenario from Liu 2014)
+    ###################################################################
+
+    # densities
+    nAr = 8.05e21     # background number density of Ar [1/m^3] (corresponds to p = 250 mTorr)
+    np0 = 8e16        # "nominal" electron density [1/m^3]
+
+    # masses
+    # me = 9.10938356e-31        # mass of an electron [kg]
+    # me = 5.489e-4              # mass of an electron [u]
+    me = 0.511e6                 # mass of an electron [eV/c2]
+    # mAr = 39.948               # mass of an argon atom [u]
+    # mAr = 39.948 * 1.66054e-27 # mass of an argon atom [kg]
+    mAr = 37.2158e9              # mass of an argon atom [eV/c2]
+    # u = 931.4941e6             # eV/c2
+    c = 299792458                # speed of light [m/s]
+    se = 40                      # momentum cross section [A^2]
+
+    # nominal electron energy
+    e0 = 1.0  # [eV]
+
+    # pressure
+    p  = 33.3*1.5      # [J/m^3] *1.5 to convert it to energy (250 mTorr)
+
+    # gas energy at the wall
+    Tg0 = 0.038778    # 3/2*300K*kB ~ (p0 - nT[:,0])/ntot
+
+    # characteristics of driving voltage
+    V0  = inputV0                 # amplitude of driving voltage [V]
+    verticalShift = inputVDC      # DC voltage (vertical shift in driving voltage)
+    tau = (1./13.56e6)             # period of driving voltage [s]
+    L   = 2.00*0.005              # half-gap-width [m] (gap width is 2 cm)
+    electrodeArea = np.pi*0.05**2 # electrode area [m^2] (electrode diameter = 0.1 m)
+
+    # transport parameters
+    nmue = 9.66e21   # argon number density times electron mobility [1/(V*cm*s)]
+    nmum = 0.0
+    nmur = 0.0
+    nmu4p = 0.0
+    nmui = 8.0e19
+    nDe  = 3.86e22   # argon number density times electron diffusivity [1/(cm*s)]
+    nDi  = 2.07e18   # argon number density times ion diffusivity [1/(cm*s)]
+    nDm  = 2.42e18   # argon number density times AR(m) diffusivity [1/(cm*s)]
+    nDr  = 2.42e18
+    nD4p = 2.42e18
+
+    # reaction parameters (NB: k_i = Ck*Ee^B*exp(-A/Ee))
+    #                          Ee = 3/2*Te (Te in eV)
+    #                          -> k_i = [Ck*(2/3)^B] * Ee^B * exp[-(3/2)*A/Ee]
+    # nominal
+    Ck = np.array([2.0e-13,2.1e-15,5.0e-16,6.4e-16,2.1e-21,1.32e8,1.72e7,1.50e7,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0e-18,4.0e-19,0.0,0.0,0.0,0.0,2.5e-17,2.5e-17,1.0e-15,1.0e-15,0.0,0.0]) # pre-exponential factors [m^3/s]
+    B  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-0.5,0,0,0,0,0,0,0,0,0,0]) # Temperature Power
+    A  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]) # activation temperature [eV]
+    dH = np.array([0.0,-7.541,-10.577,-7.393,0.0,0.0,0.0,0.0,11.577,11.725,13.168,15.76,-11.577,4.183,0.148,1.592,2.592,-1.444,-1.592,-11.725,-0.148,1.444,0.0,0.0,-4.183,-4.035,-2.592,-15.76,0.0,0.0,-8.985,-9.133,4.035,-13.168]) # energy lost per electron due to ionization rxn [eV]
+    dEps = np.array([0.0,15.76,11.577,11.725,13.168,0.0]) # E, AR+, AR(m), AR(r), AR(4p), AR
+
+    # BC parameters
+    # ks = 1.19e7  # electron recombination rate [cm/s]
+    ks = 1.366109824889323e7 # electron recombination rate [cm/s/eV]
+
+    ###################################################################
+    # Constants of nature (probably shouldn't change unless you have
+    # root privileges on universe)
+    ###################################################################
+    qe   = 1.6e-19   # unit charge [C]
+    eps0 = 8.86e-12  # permittivity of free space [F/m]
+    kB   = 1.38e-23  # Boltzmann constant [J/K]
+    # kB   = 8.62e-5 # Boltzmann constant [eV/K]
+
+
+    ###################################################################
+    # Calculate non-dimensional parameters
+    ###################################################################
+
+    # 1) Convert input units to base SI (except eV)
+    nDe  *= 100. # 1/(m*s)
+    nDi  *= 100. # 1/(m*s)
+    nDm  *= 100.
+    nDr  *= 100.
+    nD4p *= 100.
+    nmue *= 100. # 1/(V*m*s)
+    nmui *= 100. # 1/(V*m*s)
+    ks   *= 0.01 # m/s
+    se   *= 1.0e-20  # m^2
+
+    # 2) Compute "raw" transport parameters
+    De  = nDe/nAr
+    Di  = nDi/nAr
+    Dm  = nDm/nAr
+    Dr  = nDr/nAr
+    D4p = nD4p/nAr
+
+    mue = nmue/nAr
+    mui = nmui/nAr
+    mum = nmum/nAr
+    mur = nmur/nAr
+    mu4p = nmu4p/nAr
+
+    # 3) Compute non-dimensional properties required by solver
+    De    = De*tau/(L*L)
+    Di    = Di*tau/(L*L)
+    Dm    = Dm*tau/(L*L)
+    Dr    = Dr*tau/(L*L)
+    D4p   = D4p*tau/(L*L)
+
+    mue   = mue*V0*tau/(L*L)
+    mui   = mui*V0*tau/(L*L)
+    mum   = mum*V0*tau/(L*L)
+    mur   = mur*V0*tau/(L*L)
+    mu4p  = mu4p*V0*tau/(L*L)
+
+    Ck[0:4]  *= tau*np0
+    Ck[4]    *= tau*nAr
+    Ck[5:8]  *= tau
+    Ck[8:12] *= tau*nAr
+    Ck[12:24]  *= tau*np0
+    Ck[24:28] *= tau*np0*np0
+    Ck[28:30] *= tau*nAr
+    Ck[30:] *= tau*np0
+    A        = A*1.5/e0  # 1.5 to convert from temperature to energy
+    dH       = dH/e0
+    qStar    = V0/e0 # qe*V0/e0, since e0 in eV, need qe*V0 in eV, which is just V0 in V
+    alpha    = qe*np0*L*L/(V0*eps0)
+    ks       = ks*tau/L
+    p0       = p/qe/np0
+    kappaB   = 4.878171165833662*1.6129 # non-dimensional thermal conductivity of background specie
+                                # (2/3)*tau/L**2*Kb/np0/kB,
+                                # where Kb is the thermal conductivity of background specie
+
+    params.beta = np.array([[0,1,1,1,0,0,0,0,1,1,1,2,1,2,1,1,2,1,1,1,1,1,0,0,1,1,1,1,0,0,1,1,2,1],                     # E
+                            [0,1,1,1,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0],                     # AR+
+                            [0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,1,0,0,0,0,0],                     # AR(m)
+                            [0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0],                     # AR(r)
+                            [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0],                     # AR(4p)
+                            [2,1,1,1,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1]], dtype=np.int64)    # AR
+
+    params.alfa = np.array([[0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,0,0,0,0,1,1],                     # E
+                            [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0],                     # AR+
+                            [2,1,0,2,1,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],                     # AR(m)
+                            [0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,1,0],                     # AR(r)
+                            [0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,0,1],                     # AR(4p)
+                            [0,0,0,0,1,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0]], dtype=np.int64)    # AR
+    
+    # Rxn1:  2AR(m)         ->   2AR
+    # Rxn2:  AR(m) + AR(r)  ->   E + AR+ + AR
+    # Rxn3:  2AR(4p)        ->   E + AR+ + AR
+    # Rxn4:  2AR(m)         ->   E + AR+ + AR
+    # Rxn5:  AR(m) + AR     ->   2AR
+    # Rxn6:  AR(r)          ->   AR + hv
+    # Rxn7:  AR(4p)         ->   AR(m) + hv
+    # Rxn8:  AR(4p)         ->   AR(r) + hv
+    # Rxn9:  E + AR         ->   E + AR(m)
+    # Rxn10: E + AR         ->   E + AR(r)
+    # Rxn11: E + AR         ->   E + AR(4p)
+    # Rxn12: E + AR         ->   2E + AR+
+    # Rxn13: E + AR(m)      ->   E + AR
+    # Rxn14: E + AR(m)      ->   2E + AR+
+    # Rxn15: E + AR(m)      ->   E + AR(r)
+    # Rxn16: E + AR(m)      ->   E + AR(4p)
+    # Rxn17: E + AR(4p)     ->   2E + AR+
+    # Rxn18: E + AR(4p)     ->   E + AR(r)
+    # Rxn19: E + AR(4p)     ->   E + AR(m)
+    # Rxn20: E + AR(r)      ->   E + AR
+    # Rxn21: E + AR(r)      ->   E + AR(m)
+    # Rxn22: E + AR(r)      ->   E + AR(4p)
+    # Rxn23: E + AR+        ->   AR(m) + hv
+    # Rxn24: E + AR+        ->   AR(4p) + hv
+    # Rxn25: 2E + AR+       ->   E + AR(m)
+    # Rxn26: 2E + AR+       ->   E + AR(r)
+    # Rxn27: 2E + AR+       ->   E + AR(4p)
+    # Rxn28: 2E + AR+       ->   E + AR
+    # Rxn29: AR + AR(4p)    ->   AR + AR(m)
+    # Rxn30: AR + AR(4p)    ->   AR + AR(r)
+    # Rxn31: AR(m) + AR(4p) ->   E + AR+ + AR
+    # Rxn32: AR(r) + AR(4p) ->   E + AR+ + AR
+    # Rxn33: E + AR(r)      ->   2E + AR+
+    # Rxn34: E + AR(4p)     ->   E + AR
+
+    rxnNameDict = { 0: "2Ar(m) => 2Ar",
+                    1: "Ar(m) + Ar(r) => E + Ar+ + Ar",
+                    2: "2Ar(4p) => E + Ar+ + Ar",
+                    3: "2Ar(m) => E + Ar+ + Ar",
+                    4: "Ar(m) + Ar => 2Ar",
+                    5: "Ar(r) => Ar",
+                    6: "Ar(4p) => Ar(m)",
+                    7: "Ar(4p) => Ar(r)",
+                    8: "Excitation_Metastable",
+                    9: "Excitation_Resonant",
+                   10: "Excitation_4p",
+                   11: "Ionization",
+                   12: "DeExcitation_Metastable",
+                   13: "StepIonization_Metastable",
+                   14: "E + Ar(m) => E + Ar(r)",
+                   15: "E + Ar(m) => E + Ar(4p)",
+                   16: "StepIonization_4p",
+                   17: "E + Ar(4p) => E + Ar(r)",
+                   18: "E + Ar(4p) => E + Ar(m)",
+                   19: "DeExcitation_Resonant",
+                   20: "E + Ar(r) => E + Ar(m)",
+                   21: "E + Ar(r) => E + Ar(4p)",
+                   22: "E + Ar+ => Ar(m)",
+                   23: "E + Ar+ => Ar(4p)",
+                   24: "3BdyRecomb_Metastable",
+                   25: "3BdyRecomb_Resonant",
+                   26: "3BdyRecomb_4p",
+                   27: "3BdyRecomb_Ground",
+                   28: "Ar + Ar(4p) => Ar + Ar(m)",
+                   29: "Ar + Ar(4p) => Ar + Ar(r)",
+                   30: "Ar(m) + Ar(4p) => E + Ar+ + Ar",
+                   31: "Ar(r) + Ar(4p) => E + Ar+ + Ar",
+                   32: "StepIonization_Resonant",
+                   33: "DeExcitation_4p"}
+
+    # 4) Set values in params class
+    params.D[0]    = De
+    params.D[1]    = Di
+    params.D[2]    = Dm
+    params.D[3]    = Dr
+    params.D[4]    = D4p
+
+    params.mu[0]   = mue
+    params.mu[1]   = mui
+    params.mu[2]   = mum
+    params.mu[3]   = mur
+    params.mu[4]   = mu4p
+
+    params.A[:]    = Ck[:]
+    params.B[:]    = B[:]
+    params.C[:]    = A[:]
+
+    # Account for the 2/3 term to convert from electron temperature to electron energy
+    for i in range(len(params.A)):
+        params.A[i] *= (2/3)**(params.B[i])
+
+    params.dH[:]   = dH[:]
+    params.dEps[:] = dEps[:]
+    params.qStar   = qStar
+    params.alpha   = alpha
+    params.ks      = ks
+    params.gam     = gam
+    params.kappaB  = kappaB
+    params.nAronp0 = nAr / np0
+    params.p0      = p0
+    params.Tg0     = Tg0
+    params.EC      = 2.0 * me / mAr \
+        * np.sqrt(16.0 * (me + mAr) * e0 * c**2
+                  / (3.0 * np.pi * me * mAr)) * se * nAr * tau
+    # params.EC = 2.0 * me / mAr * 3.8e9 * tau
+
+    params.verticalShift = verticalShift / V0
+
+    # Parameters needed to compute the current with dimensions
+    params.V0Ltau  = V0 / (L * tau)
+    params.V0L     = V0 / L
+    params.LLV0tau = (L*L) / (V0*tau)
+    params.tauL    = L / tau
+    params.np0     = np0           # "nominal" electron density [1/m^3]
+    params.qe      = qe            # unit charge [C]
+    params.eps0    = eps0          # unit charge [C]
+    params.eArea   = electrodeArea # electrode area [m^2]
+
+
+    reactionExpressionslist = [f"{params.A[0]} * energy**{params.B[0]} * np.exp(-{params.C[0]} / energy)",
+                               f"{params.A[1]} * energy**{params.B[1]} * np.exp(-{params.C[1]} / energy)",
+                               f"{params.A[2]} * energy**{params.B[2]} * np.exp(-{params.C[2]} / energy)",
+                               f"{params.A[3]} * energy**{params.B[3]} * np.exp(-{params.C[3]} / energy)",
+                               f"{params.A[4]} * energy**{params.B[4]} * np.exp(-{params.C[4]} / energy)",
+                               f"{params.A[5]} * energy**{params.B[5]} * np.exp(-{params.C[5]} / energy)",
+                               f"{params.A[6]} * energy**{params.B[6]} * np.exp(-{params.C[6]} / energy)",
+                               f"{params.A[7]} * energy**{params.B[7]} * np.exp(-{params.C[7]} / energy)",
+                               f"{params.A[8]} * energy**{params.B[8]} * np.exp(-{params.C[8]} / energy)",
+                               f"{params.A[9]} * energy**{params.B[9]} * np.exp(-{params.C[9]} / energy)",
+                               f"{params.A[10]} * energy**{params.B[10]} * np.exp(-{params.C[10]} / energy)",
+                               f"{params.A[11]} * energy**{params.B[11]} * np.exp(-{params.C[11]} / energy)",
+                               f"{params.A[12]} * energy**{params.B[12]} * np.exp(-{params.C[12]} / energy)",
+                               f"{params.A[13]} * energy**{params.B[13]} * np.exp(-{params.C[13]} / energy)",
+                               f"{params.A[14]} * energy**{params.B[14]} * np.exp(-{params.C[14]} / energy)",
+                               f"{params.A[15]} * energy**{params.B[15]} * np.exp(-{params.C[15]} / energy)",
+                               f"{params.A[16]} * energy**{params.B[16]} * np.exp(-{params.C[16]} / energy)",
+                               f"{params.A[17]} * energy**{params.B[17]} * np.exp(-{params.C[17]} / energy)",
+                               f"{params.A[18]} * energy**{params.B[18]} * np.exp(-{params.C[18]} / energy)",
+                               f"{params.A[19]} * energy**{params.B[19]} * np.exp(-{params.C[19]} / energy)",
+                               f"{params.A[20]} * energy**{params.B[20]} * np.exp(-{params.C[20]} / energy)",
+                               f"{params.A[21]} * energy**{params.B[21]} * np.exp(-{params.C[21]} / energy)",
+                               f"{params.A[22]} * energy**{params.B[22]} * np.exp(-{params.C[22]} / energy)",
+                               f"{params.A[23]} * energy**{params.B[23]} * np.exp(-{params.C[23]} / energy)",
+                               f"{params.A[24]} * energy**{params.B[24]} * np.exp(-{params.C[24]} / energy)",
+                               f"{params.A[25]} * energy**{params.B[25]} * np.exp(-{params.C[25]} / energy)",
+                               f"{params.A[26]} * energy**{params.B[26]} * np.exp(-{params.C[26]} / energy)",
+                               f"{params.A[27]} * energy**{params.B[27]} * np.exp(-{params.C[27]} / energy)",
+                               f"{params.A[28]} * energy**{params.B[28]} * np.exp(-{params.C[28]} / energy)",
+                               f"{params.A[29]} * energy**{params.B[29]} * np.exp(-{params.C[29]} / energy)",
+                               f"{params.A[30]} * energy**{params.B[30]} * np.exp(-{params.C[30]} / energy)",
+                               f"{params.A[31]} * energy**{params.B[31]} * np.exp(-{params.C[31]} / energy)",
+                               f"{params.A[32]} * energy**{params.B[32]} * np.exp(-{params.C[32]} / energy)",
+                               f"{params.A[33]} * energy**{params.B[33]} * np.exp(-{params.C[33]} / energy)"]
+
+
+    reactionTExpressionslist = [f"{params.A[0]} * (energy**({params.B[0]}-1)) * np.exp(-{params.C[0]}/energy) * ({params.B[0]} + {params.C[0]}/energy)",
+                                f"{params.A[1]} * (energy**({params.B[1]}-1)) * np.exp(-{params.C[1]}/energy) * ({params.B[1]} + {params.C[1]}/energy)",
+                                f"{params.A[2]} * (energy**({params.B[2]}-1)) * np.exp(-{params.C[2]}/energy) * ({params.B[2]} + {params.C[2]}/energy)",
+                                f"{params.A[3]} * (energy**({params.B[3]}-1)) * np.exp(-{params.C[3]}/energy) * ({params.B[3]} + {params.C[3]}/energy)",
+                                f"{params.A[4]} * (energy**({params.B[4]}-1)) * np.exp(-{params.C[4]}/energy) * ({params.B[4]} + {params.C[4]}/energy)",
+                                f"{params.A[5]} * (energy**({params.B[5]}-1)) * np.exp(-{params.C[5]}/energy) * ({params.B[5]} + {params.C[5]}/energy)",
+                                f"{params.A[6]} * (energy**({params.B[6]}-1)) * np.exp(-{params.C[6]}/energy) * ({params.B[6]} + {params.C[6]}/energy)",
+				f"{params.A[7]} * (energy**({params.B[7]}-1)) * np.exp(-{params.C[7]}/energy) * ({params.B[7]} + {params.C[7]}/energy)",
+		                f"{params.A[8]} * (energy**({params.B[8]}-1)) * np.exp(-{params.C[8]}/energy) * ({params.B[8]} + {params.C[8]}/energy)",
+                                f"{params.A[9]} * (energy**({params.B[9]}-1)) * np.exp(-{params.C[9]}/energy) * ({params.B[9]} + {params.C[9]}/energy)",
+                                f"{params.A[10]} * (energy**({params.B[10]}-1)) * np.exp(-{params.C[10]}/energy) * ({params.B[10]} + {params.C[10]}/energy)",
+                                f"{params.A[11]} * (energy**({params.B[11]}-1)) * np.exp(-{params.C[11]}/energy) * ({params.B[11]} + {params.C[11]}/energy)",
+                                f"{params.A[12]} * (energy**({params.B[12]}-1)) * np.exp(-{params.C[12]}/energy) * ({params.B[12]} + {params.C[12]}/energy)",
+                                f"{params.A[13]} * (energy**({params.B[13]}-1)) * np.exp(-{params.C[13]}/energy) * ({params.B[13]} + {params.C[13]}/energy)",
+                                f"{params.A[14]} * (energy**({params.B[14]}-1)) * np.exp(-{params.C[14]}/energy) * ({params.B[14]} + {params.C[14]}/energy)",
+                                f"{params.A[15]} * (energy**({params.B[15]}-1)) * np.exp(-{params.C[15]}/energy) * ({params.B[15]} + {params.C[15]}/energy)",
+                                f"{params.A[16]} * (energy**({params.B[16]}-1)) * np.exp(-{params.C[16]}/energy) * ({params.B[16]} + {params.C[16]}/energy)",
+                                f"{params.A[17]} * (energy**({params.B[17]}-1)) * np.exp(-{params.C[17]}/energy) * ({params.B[17]} + {params.C[17]}/energy)",
+                                f"{params.A[18]} * (energy**({params.B[18]}-1)) * np.exp(-{params.C[18]}/energy) * ({params.B[18]} + {params.C[18]}/energy)",
+                                f"{params.A[19]} * (energy**({params.B[19]}-1)) * np.exp(-{params.C[19]}/energy) * ({params.B[19]} + {params.C[19]}/energy)",
+                                f"{params.A[20]} * (energy**({params.B[20]}-1)) * np.exp(-{params.C[20]}/energy) * ({params.B[20]} + {params.C[20]}/energy)",
+                                f"{params.A[21]} * (energy**({params.B[21]}-1)) * np.exp(-{params.C[21]}/energy) * ({params.B[21]} + {params.C[21]}/energy)",
+                                f"{params.A[22]} * (energy**({params.B[22]}-1)) * np.exp(-{params.C[22]}/energy) * ({params.B[22]} + {params.C[22]}/energy)",
+                                f"{params.A[23]} * (energy**({params.B[23]}-1)) * np.exp(-{params.C[23]}/energy) * ({params.B[23]} + {params.C[23]}/energy)",
+                                f"{params.A[24]} * (energy**({params.B[24]}-1)) * np.exp(-{params.C[24]}/energy) * ({params.B[24]} + {params.C[24]}/energy)",
+                                f"{params.A[25]} * (energy**({params.B[25]}-1)) * np.exp(-{params.C[25]}/energy) * ({params.B[25]} + {params.C[25]}/energy)",
+                                f"{params.A[26]} * (energy**({params.B[26]}-1)) * np.exp(-{params.C[26]}/energy) * ({params.B[26]} + {params.C[26]}/energy)",
+                                f"{params.A[27]} * (energy**({params.B[27]}-1)) * np.exp(-{params.C[27]}/energy) * ({params.B[27]} + {params.C[27]}/energy)",
+                                f"{params.A[28]} * (energy**({params.B[28]}-1)) * np.exp(-{params.C[28]}/energy) * ({params.B[28]} + {params.C[28]}/energy)",
+                                f"{params.A[29]} * (energy**({params.B[29]}-1)) * np.exp(-{params.C[29]}/energy) * ({params.B[29]} + {params.C[29]}/energy)",
+                                f"{params.A[30]} * (energy**({params.B[30]}-1)) * np.exp(-{params.C[30]}/energy) * ({params.B[30]} + {params.C[30]}/energy)",
+                                f"{params.A[31]} * (energy**({params.B[31]}-1)) * np.exp(-{params.C[31]}/energy) * ({params.B[31]} + {params.C[31]}/energy)",
+                                f"{params.A[32]} * (energy**({params.B[32]}-1)) * np.exp(-{params.C[32]}/energy) * ({params.B[32]} + {params.C[32]}/energy)",
+                                f"{params.A[33]} * (energy**({params.B[33]}-1)) * np.exp(-{params.C[33]}/energy) * ({params.B[33]} + {params.C[33]}/energy)"]
+           
+
+    reactionExpressionTypelist =  np.array([False,False,False,False,False,False,False,False, # Rxns 1-8
+                                           True,True,True,True,True,True,True,True,True,True,True,True,True,True, # Rxns 9-21
+                                           False,False,True,True,True,True,False,False,False,False,True,True]) # Rxns 22-34
+
+    thresholded_rxn = np.array([False, False, False, False, False, False, False, False,
+                                True, True, True, True, False, True, True, True, True, False, False, False, False,
+                                True, False, False, False, False, False, False, False, False, False, False, True,
+                                False])
+    reactionsList = []
+    LOGFilename = 'interpolationSample.log'
+    f = open(LOGFilename, 'w')
+
+    for i in range(Nr):
+        if reactionExpressionTypelist[i]:
+            if i < 14 or i == 16 or i == 19 or i > 23:
+                f = h5.File("../../../BOLSIGChemistry_NominalRates/{0:s}.h5".format(rxnNameDict[i]), 'r')
+                dataset = f["table"]
+            else:
+                f = h5.File("../../../BOLSIGChemistry_NominalRates/StepExcitation.h5", 'r')
+                dataset = f[rxnNameDict[i]]
+
+            Te = dataset[:,0]
+            Te /= 11604
+            rateCoeff = dataset[:,1]
+            if i > 23 and i < 28:
+                rateCoeff /= 6.022e23**2
+            else:
+                rateCoeff /= 6.022e23
+
+            ## Removing BOLSIG failures
+            fail_inds = []
+            for j in range(len(rateCoeff)):
+                if rateCoeff[j] == 0.0 and j > np.nonzero(rateCoeff)[0][0]:
+                    fail_inds.append(j)
+
+            Te = np.delete(Te, fail_inds)
+            rateCoeff = np.delete(rateCoeff, fail_inds)
+
+            # Sorting mean energy array and rate coefficient array based on
+            # the mean energy array.
+            Teinds = Te.argsort()
+            rateCoeff = rateCoeff[Teinds]
+            Te = Te[Teinds]
+
+            # Find duplicates
+            TeDuplicateinds = np.where(np.abs(np.diff(Te, axis=0)) > 0.0)
+            TeDuplicateindsForLog = np.where(np.abs(np.diff(Te, axis=0)) == 0.0)
+            rateCoeff = rateCoeff[TeDuplicateinds]
+            Te = Te[TeDuplicateinds]
+
+            # Nondimensionalization of mean energy.
+            Te *= 1.5
+
+            # Find first non-zero value of the coefficient rate.
+            I = np.nonzero(rateCoeff)
+
+            diffRateCoeff = [j-i for i, j in zip(rateCoeff[:-1], rateCoeff[1:])]
+            diffTe = [j-i for i, j in zip(Te[:-1], Te[1:])]
+
+            Monotonicity = np.asarray([j/i for i, j in zip(diffTe, diffRateCoeff)])
+            Monotonicity = np.insert(Monotonicity, 0, 0.0, axis=0)
+
+            Nan = np.isnan(Monotonicity)
+            Inf = np.isinf(Monotonicity)
+            if thresholded_rxn[i] == True:
+                indexPositive = np.where(Monotonicity>0.0)
+            else:
+                indexPositive = np.where(Monotonicity<0.0)
+            Positive = np.full(Monotonicity.shape, False, dtype=bool)
+            Positive[indexPositive] = True
+            
+            indices = Nan + Inf + Positive
+
+
+            #lastFalse = np.where(indices==False)[-1][-1] + 2
+            for k in range(len(Te)):
+               if Te[k] < 4.5 and indices[k] == False:
+                  lastFalse = k + 2
+
+            # Transformation to log scale.
+            TeLog = np.log(Te)
+
+            # Compute the slope of the rate coefficient between its first two non-zero values.
+            # Finite differences are used.
+            dydx = (rateCoeff[lastFalse + 1] - rateCoeff[lastFalse]) \
+                 / (Te[lastFalse + 1] - Te[lastFalse])
+
+            # Arrhenius form: kf = A * exp(-C / Te)
+            C = Te[lastFalse]**2.0*dydx / rateCoeff[lastFalse]
+
+            # Compute pre-exponential coefficient, A, in log scale.
+            ALog = np.log(rateCoeff[lastFalse]) + C / Te[lastFalse]
+
+            # Transform rate coefficient in log scale.
+            rateCoeffLog = np.zeros(rateCoeff.shape)
+            rateCoeffLog[lastFalse:] = np.log(rateCoeff[lastFalse:])
+            # For the troublesome values, we use the Arrhenius form.
+            rateCoeffLog[0:lastFalse] = ALog - C / Te[0:lastFalse]
+            # Nondimensionalization in log scale.
+            if (i < 12):
+                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
+            elif i > 23 and i < 28:
+                rateCoeffLog += - np.log(1.0/tau) + 2*np.log(np0)
+            else:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(np0)
+
+            # Interpolation in log scale.
+            reactionExpressionsLog = CubicSpline(TeLog, rateCoeffLog)
+            # Gradient in log scale
+            reactionTExpressionsLog = CubicSpline.derivative(reactionExpressionsLog)
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf_log = reactionExpressionsLog,
+                                kf_T_log = reactionTExpressionsLog)
+            reactionsList.append(reaction)
+
+            logging.basicConfig(filename=LOGFilename)
+            logging.warning('Interpolation info (Reaction %s):', i + 1)
+            logging.warning('First non-zero entry in the rate coefficient: %s', I[0][0])
+            logging.warning('Monotonicity of the rate coefficient start from entry: %s', lastFalse)
+            logging.warning('Position of possible duplicates in mean energy array: %s',
+                          TeDuplicateindsForLog[0])
+
+        else:
+            rxn   = eval("lambda energy :" + reactionExpressionslist[i])
+            rxn_T = eval("lambda energy :" + reactionTExpressionslist[i])
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf = rxn, kf_T = rxn_T)
+            reactionsList.append(reaction)
+
+    params.reactionsList = reactionsList
+
+    ## Electron Transport Data
+    diffList = []
+    transport = h5.File("../../../BOLSIGChemistry_NominalRates/nominal_transport.h5", 'r')
+    NDe_v_Te = transport["diffusivity"]
+    Te_trans = NDe_v_Te[:,0]
+    Te_trans /= 11604
+    De_interp = (NDe_v_Te[:,1]/nAr)*tau/(L*L)
+    De_spline = CubicSpline(Te_trans, De_interp)
+    De_Te_spline = CubicSpline.derivative(De_spline)
+    diffusivity = Diffusivity(interpolate = True, D_expression = De_spline, D_T_expression = De_Te_spline)
+    diffList.append(diffusivity)
+
+    Ns = 6
+    for i in range(1, Ns):
+        diffList.append(Diffusivity(interpolate = False))
+
+    params.diffusivityList = diffList
+
+    muList = []
+    Nmue_v_Te = transport["mobility"]
+    mue_interp = (Nmue_v_Te[:,1]/nAr)*V0*tau/(L*L)
+    mue_spline = CubicSpline(Te_trans, mue_interp)
+    mue_Te_spline = CubicSpline.derivative(mue_spline)
+    mobility = Mobility(interpolate = True, mu_expression = mue_spline, mu_T_expression = mue_Te_spline)
+    muList.append(mobility)
+
+    Ns = 6
+    for i in range(1, Ns):
+        muList.append(Mobility(interpolate = False))
+
+    params.mobilityList = muList
+    
+    # 5) Dump to screen
+    params.print()
diff --git a/psaapProperties_6Species_2Torr_Expanded.py b/psaapProperties_6Species_2Torr_Expanded.py
new file mode 100755
index 000000000..15233db2a
--- /dev/null
+++ b/psaapProperties_6Species_2Torr_Expanded.py
@@ -0,0 +1,549 @@
+import numpy as np
+from scipy.interpolate import CubicSpline
+import csv
+#import matplotlib.pyplot as plt
+#import matplotlib.colors as mcolors
+import h5py as h5
+
+import logging
+
+class Reaction(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Diffusivity(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Mobility(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+
+def setPsaapProperties_6Species_2Torr_Expanded(gam, inputV0, inputVDC, params, Nr, iSample):
+    """Sets non-dimensional properties corresponding to Liu 2014 paper.
+
+    Inputs:
+      gam : Secondary electron emission coefficient
+      params : chebSolver.modelParams class
+
+    Outputs: None
+      params data is overwritten using values from Liu 2014.
+    """
+    ###################################################################
+    # User specified parameters (you may change these if you wish to
+    # run a different scenario from Liu 2014)
+    ###################################################################
+
+    # densities
+    nAr = 8.05e22     # background number density of Ar [1/m^3] (corresponds to p = 2.5 Torr)
+    np0 = 8e16        # "nominal" electron density [1/m^3]
+
+    # masses
+    # me = 9.10938356e-31        # mass of an electron [kg]
+    # me = 5.489e-4              # mass of an electron [u]
+    me = 0.511e6                 # mass of an electron [eV/c2]
+    # mAr = 39.948               # mass of an argon atom [u]
+    # mAr = 39.948 * 1.66054e-27 # mass of an argon atom [kg]
+    mAr = 37.2158e9              # mass of an argon atom [eV/c2]
+    # u = 931.4941e6             # eV/c2
+    c = 299792458                # speed of light [m/s]
+    se = 40                      # momentum cross section [A^2]
+
+    # nominal electron energy
+    e0 = 1.0  # [eV]
+
+    # pressure
+    p  = 333.3*1.5      # [J/m^3] *1.5 to convert it to energy (2.5 Torr)
+
+    # gas energy at the wall
+    Tg0 = 0.038778    # 3/2*300K*kB ~ (p0 - nT[:,0])/ntot
+
+    # characteristics of driving voltage
+    V0  = inputV0                 # amplitude of driving voltage [V]
+    verticalShift = inputVDC      # DC voltage (vertical shift in driving voltage)
+    tau = (1./13.56e6)             # period of driving voltage [s]
+    L   = 2.00*0.005              # half-gap-width [m] (gap width is 2 cm)
+    electrodeArea = np.pi*0.05**2 # electrode area [m^2] (electrode diameter = 0.1 m)
+
+    # transport parameters
+    nmue = 9.66e21   # argon number density times electron mobility [1/(V*cm*s)]
+    nmum = 0.0
+    nmur = 0.0
+    nmu4p = 0.0
+    nmui = 8.0e19
+    nDe  = 3.86e22   # argon number density times electron diffusivity [1/(cm*s)]
+    nDi  = 2.07e18   # argon number density times ion diffusivity [1/(cm*s)]
+    nDm  = 2.42e18   # argon number density times AR(m) diffusivity [1/(cm*s)]
+    nDr  = 2.42e18
+    nD4p = 2.42e18
+
+    # reaction parameters (NB: k_i = Ck*Ee^B*exp(-A/Ee))
+    #                          Ee = 3/2*Te (Te in eV)
+    #                          -> k_i = [Ck*(2/3)^B] * Ee^B * exp[-(3/2)*A/Ee]
+    # nominal
+    Ck = np.array([2.0e-13,2.1e-15,5.0e-16,6.4e-16,2.1e-21,1.32e8,1.72e7,1.50e7,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0e-18,4.0e-19,0.0,0.0,0.0,0.0,2.5e-17,2.5e-17,1.0e-15,1.0e-15,0.0,0.0]) # pre-exponential factors [m^3/s]
+    B  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-0.5,0,0,0,0,0,0,0,0,0,0]) # Temperature Power
+    A  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]) # activation temperature [eV]
+    dH = np.array([0.0,-7.541,-10.577,-7.393,0.0,0.0,0.0,0.0,11.577,11.725,13.168,15.76,-11.577,4.183,0.148,1.592,2.592,-1.444,-1.592,-11.725,-0.148,1.444,0.0,0.0,-4.183,-4.035,-2.592,-15.76,0.0,0.0,-8.985,-9.133,4.035,-13.168]) # energy lost per electron due to ionization rxn [eV]
+    dEps = np.array([0.0,15.76,11.577,11.725,13.168,0.0]) # E, AR+, AR(m), AR(r), AR(4p), AR
+
+    # BC parameters
+    # ks = 1.19e7  # electron recombination rate [cm/s]
+    ks = 1.366109824889323e7 # electron recombination rate [cm/s/eV]
+
+    ###################################################################
+    # Constants of nature (probably shouldn't change unless you have
+    # root privileges on universe)
+    ###################################################################
+    qe   = 1.6e-19   # unit charge [C]
+    eps0 = 8.86e-12  # permittivity of free space [F/m]
+    kB   = 1.38e-23  # Boltzmann constant [J/K]
+    # kB   = 8.62e-5 # Boltzmann constant [eV/K]
+
+
+    ###################################################################
+    # Calculate non-dimensional parameters
+    ###################################################################
+
+    # 1) Convert input units to base SI (except eV)
+    nDe  *= 100. # 1/(m*s)
+    nDi  *= 100. # 1/(m*s)
+    nDm  *= 100.
+    nDr  *= 100.
+    nD4p *= 100.
+    nmue *= 100. # 1/(V*m*s)
+    nmui *= 100. # 1/(V*m*s)
+    ks   *= 0.01 # m/s
+    se   *= 1.0e-20  # m^2
+
+    # 2) Compute "raw" transport parameters
+    De  = nDe/nAr
+    Di  = nDi/nAr
+    Dm  = nDm/nAr
+    Dr  = nDr/nAr
+    D4p = nD4p/nAr
+
+    mue = nmue/nAr
+    mui = nmui/nAr
+    mum = nmum/nAr
+    mur = nmur/nAr
+    mu4p = nmu4p/nAr
+
+    # 3) Compute non-dimensional properties required by solver
+    De    = De*tau/(L*L)
+    Di    = Di*tau/(L*L)
+    Dm    = Dm*tau/(L*L)
+    Dr    = Dr*tau/(L*L)
+    D4p   = D4p*tau/(L*L)
+
+    mue   = mue*V0*tau/(L*L)
+    mui   = mui*V0*tau/(L*L)
+    mum   = mum*V0*tau/(L*L)
+    mur   = mur*V0*tau/(L*L)
+    mu4p  = mu4p*V0*tau/(L*L)
+
+    Ck[0:4]  *= tau*np0
+    Ck[4]    *= tau*nAr
+    Ck[5:8]  *= tau
+    Ck[8:12] *= tau*nAr
+    Ck[12:24]  *= tau*np0
+    Ck[24:28] *= tau*np0*np0
+    Ck[28:30] *= tau*nAr
+    Ck[30:] *= tau*np0
+    A        = A*1.5/e0  # 1.5 to convert from temperature to energy
+    dH       = dH/e0
+    qStar    = V0/e0 # qe*V0/e0, since e0 in eV, need qe*V0 in eV, which is just V0 in V
+    alpha    = qe*np0*L*L/(V0*eps0)
+    ks       = ks*tau/L
+    p0       = p/qe/np0
+    kappaB   = 4.878171165833662*1.6129 # non-dimensional thermal conductivity of background specie
+                                # (2/3)*tau/L**2*Kb/np0/kB,
+                                # where Kb is the thermal conductivity of background specie
+
+    params.beta = np.array([[0,1,1,1,0,0,0,0,1,1,1,2,1,2,1,1,2,1,1,1,1,1,0,0,1,1,1,1,0,0,1,1,2,1],                     # E
+                            [0,1,1,1,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0],                     # AR+
+                            [0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,1,0,0,0,0,0],                     # AR(m)
+                            [0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0],                     # AR(r)
+                            [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0],                     # AR(4p)
+                            [2,1,1,1,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1]], dtype=np.int64)    # AR
+
+    params.alfa = np.array([[0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,0,0,0,0,1,1],                     # E
+                            [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0],                     # AR+
+                            [2,1,0,2,1,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],                     # AR(m)
+                            [0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,1,0],                     # AR(r)
+                            [0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,0,1],                     # AR(4p)
+                            [0,0,0,0,1,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0]], dtype=np.int64)    # AR
+    
+    # Rxn1:  2AR(m)         ->   2AR
+    # Rxn2:  AR(m) + AR(r)  ->   E + AR+ + AR
+    # Rxn3:  2AR(4p)        ->   E + AR+ + AR
+    # Rxn4:  2AR(m)         ->   E + AR+ + AR
+    # Rxn5:  AR(m) + AR     ->   2AR
+    # Rxn6:  AR(r)          ->   AR + hv
+    # Rxn7:  AR(4p)         ->   AR(m) + hv
+    # Rxn8:  AR(4p)         ->   AR(r) + hv
+    # Rxn9:  E + AR         ->   E + AR(m)
+    # Rxn10: E + AR         ->   E + AR(r)
+    # Rxn11: E + AR         ->   E + AR(4p)
+    # Rxn12: E + AR         ->   2E + AR+
+    # Rxn13: E + AR(m)      ->   E + AR
+    # Rxn14: E + AR(m)      ->   2E + AR+
+    # Rxn15: E + AR(m)      ->   E + AR(r)
+    # Rxn16: E + AR(m)      ->   E + AR(4p)
+    # Rxn17: E + AR(4p)     ->   2E + AR+
+    # Rxn18: E + AR(4p)     ->   E + AR(r)
+    # Rxn19: E + AR(4p)     ->   E + AR(m)
+    # Rxn20: E + AR(r)      ->   E + AR
+    # Rxn21: E + AR(r)      ->   E + AR(m)
+    # Rxn22: E + AR(r)      ->   E + AR(4p)
+    # Rxn23: E + AR+        ->   AR(m) + hv
+    # Rxn24: E + AR+        ->   AR(4p) + hv
+    # Rxn25: 2E + AR+       ->   E + AR(m)
+    # Rxn26: 2E + AR+       ->   E + AR(r)
+    # Rxn27: 2E + AR+       ->   E + AR(4p)
+    # Rxn28: 2E + AR+       ->   E + AR
+    # Rxn29: AR + AR(4p)    ->   AR + AR(m)
+    # Rxn30: AR + AR(4p)    ->   AR + AR(r)
+    # Rxn31: AR(m) + AR(4p) ->   E + AR+ + AR
+    # Rxn32: AR(r) + AR(4p) ->   E + AR+ + AR
+    # Rxn33: E + AR(r)      ->   2E + AR+
+    # Rxn34: E + AR(4p)     ->   E + AR
+
+    rxnNameDict = { 0: "2Ar(m) => 2Ar",
+                    1: "Ar(m) + Ar(r) => E + Ar+ + Ar",
+                    2: "2Ar(4p) => E + Ar+ + Ar",
+                    3: "2Ar(m) => E + Ar+ + Ar",
+                    4: "Ar(m) + Ar => 2Ar",
+                    5: "Ar(r) => Ar",
+                    6: "Ar(4p) => Ar(m)",
+                    7: "Ar(4p) => Ar(r)",
+                    8: "Excitation_Metastable",
+                    9: "Excitation_Resonant",
+                   10: "Excitation_4p",
+                   11: "Ionization",
+                   12: "DeExcitation_Metastable",
+                   13: "StepIonization_Metastable",
+                   14: "E + Ar(m) => E + Ar(r)",
+                   15: "E + Ar(m) => E + Ar(4p)",
+                   16: "StepIonization_4p",
+                   17: "E + Ar(4p) => E + Ar(r)",
+                   18: "E + Ar(4p) => E + Ar(m)",
+                   19: "DeExcitation_Resonant",
+                   20: "E + Ar(r) => E + Ar(m)",
+                   21: "E + Ar(r) => E + Ar(4p)",
+                   22: "E + Ar+ => Ar(m)",
+                   23: "E + Ar+ => Ar(4p)",
+                   24: "3BdyRecomb_Metastable",
+                   25: "3BdyRecomb_Resonant",
+                   26: "3BdyRecomb_4p",
+                   27: "3BdyRecomb_Ground",
+                   28: "Ar + Ar(4p) => Ar + Ar(m)",
+                   29: "Ar + Ar(4p) => Ar + Ar(r)",
+                   30: "Ar(m) + Ar(4p) => E + Ar+ + Ar",
+                   31: "Ar(r) + Ar(4p) => E + Ar+ + Ar",
+                   32: "StepIonization_Resonant",
+                   33: "DeExcitation_4p"}
+
+    # 4) Set values in params class
+    params.D[0]    = De
+    params.D[1]    = Di
+    params.D[2]    = Dm
+    params.D[3]    = Dr
+    params.D[4]    = D4p
+
+    params.mu[0]   = mue
+    params.mu[1]   = mui
+    params.mu[2]   = mum
+    params.mu[3]   = mur
+    params.mu[4]   = mu4p
+
+    params.A[:]    = Ck[:]
+    params.B[:]    = B[:]
+    params.C[:]    = A[:]
+
+    # Account for the 2/3 term to convert from electron temperature to electron energy
+    for i in range(len(params.A)):
+        params.A[i] *= (2/3)**(params.B[i])
+
+    params.dH[:]   = dH[:]
+    params.dEps[:] = dEps[:]
+    params.qStar   = qStar
+    params.alpha   = alpha
+    params.ks      = ks
+    params.gam     = gam
+    params.kappaB  = kappaB
+    params.nAronp0 = nAr / np0
+    params.p0      = p0
+    params.Tg0     = Tg0
+    params.EC      = 2.0 * me / mAr \
+        * np.sqrt(16.0 * (me + mAr) * e0 * c**2
+                  / (3.0 * np.pi * me * mAr)) * se * nAr * tau
+    # params.EC = 2.0 * me / mAr * 3.8e9 * tau
+
+    params.verticalShift = verticalShift / V0
+
+    # Parameters needed to compute the current with dimensions
+    params.V0Ltau  = V0 / (L * tau)
+    params.V0L     = V0 / L
+    params.LLV0tau = (L*L) / (V0*tau)
+    params.tauL    = L / tau
+    params.np0     = np0           # "nominal" electron density [1/m^3]
+    params.qe      = qe            # unit charge [C]
+    params.eps0    = eps0          # unit charge [C]
+    params.eArea   = electrodeArea # electrode area [m^2]
+
+
+    reactionExpressionslist = [f"{params.A[0]} * energy**{params.B[0]} * np.exp(-{params.C[0]} / energy)",
+                               f"{params.A[1]} * energy**{params.B[1]} * np.exp(-{params.C[1]} / energy)",
+                               f"{params.A[2]} * energy**{params.B[2]} * np.exp(-{params.C[2]} / energy)",
+                               f"{params.A[3]} * energy**{params.B[3]} * np.exp(-{params.C[3]} / energy)",
+                               f"{params.A[4]} * energy**{params.B[4]} * np.exp(-{params.C[4]} / energy)",
+                               f"{params.A[5]} * energy**{params.B[5]} * np.exp(-{params.C[5]} / energy)",
+                               f"{params.A[6]} * energy**{params.B[6]} * np.exp(-{params.C[6]} / energy)",
+                               f"{params.A[7]} * energy**{params.B[7]} * np.exp(-{params.C[7]} / energy)",
+                               f"{params.A[8]} * energy**{params.B[8]} * np.exp(-{params.C[8]} / energy)",
+                               f"{params.A[9]} * energy**{params.B[9]} * np.exp(-{params.C[9]} / energy)",
+                               f"{params.A[10]} * energy**{params.B[10]} * np.exp(-{params.C[10]} / energy)",
+                               f"{params.A[11]} * energy**{params.B[11]} * np.exp(-{params.C[11]} / energy)",
+                               f"{params.A[12]} * energy**{params.B[12]} * np.exp(-{params.C[12]} / energy)",
+                               f"{params.A[13]} * energy**{params.B[13]} * np.exp(-{params.C[13]} / energy)",
+                               f"{params.A[14]} * energy**{params.B[14]} * np.exp(-{params.C[14]} / energy)",
+                               f"{params.A[15]} * energy**{params.B[15]} * np.exp(-{params.C[15]} / energy)",
+                               f"{params.A[16]} * energy**{params.B[16]} * np.exp(-{params.C[16]} / energy)",
+                               f"{params.A[17]} * energy**{params.B[17]} * np.exp(-{params.C[17]} / energy)",
+                               f"{params.A[18]} * energy**{params.B[18]} * np.exp(-{params.C[18]} / energy)",
+                               f"{params.A[19]} * energy**{params.B[19]} * np.exp(-{params.C[19]} / energy)",
+                               f"{params.A[20]} * energy**{params.B[20]} * np.exp(-{params.C[20]} / energy)",
+                               f"{params.A[21]} * energy**{params.B[21]} * np.exp(-{params.C[21]} / energy)",
+                               f"{params.A[22]} * energy**{params.B[22]} * np.exp(-{params.C[22]} / energy)",
+                               f"{params.A[23]} * energy**{params.B[23]} * np.exp(-{params.C[23]} / energy)",
+                               f"{params.A[24]} * energy**{params.B[24]} * np.exp(-{params.C[24]} / energy)",
+                               f"{params.A[25]} * energy**{params.B[25]} * np.exp(-{params.C[25]} / energy)",
+                               f"{params.A[26]} * energy**{params.B[26]} * np.exp(-{params.C[26]} / energy)",
+                               f"{params.A[27]} * energy**{params.B[27]} * np.exp(-{params.C[27]} / energy)",
+                               f"{params.A[28]} * energy**{params.B[28]} * np.exp(-{params.C[28]} / energy)",
+                               f"{params.A[29]} * energy**{params.B[29]} * np.exp(-{params.C[29]} / energy)",
+                               f"{params.A[30]} * energy**{params.B[30]} * np.exp(-{params.C[30]} / energy)",
+                               f"{params.A[31]} * energy**{params.B[31]} * np.exp(-{params.C[31]} / energy)",
+                               f"{params.A[32]} * energy**{params.B[32]} * np.exp(-{params.C[32]} / energy)",
+                               f"{params.A[33]} * energy**{params.B[33]} * np.exp(-{params.C[33]} / energy)"]
+
+
+    reactionTExpressionslist = [f"{params.A[0]} * (energy**({params.B[0]}-1)) * np.exp(-{params.C[0]}/energy) * ({params.B[0]} + {params.C[0]}/energy)",
+                                f"{params.A[1]} * (energy**({params.B[1]}-1)) * np.exp(-{params.C[1]}/energy) * ({params.B[1]} + {params.C[1]}/energy)",
+                                f"{params.A[2]} * (energy**({params.B[2]}-1)) * np.exp(-{params.C[2]}/energy) * ({params.B[2]} + {params.C[2]}/energy)",
+                                f"{params.A[3]} * (energy**({params.B[3]}-1)) * np.exp(-{params.C[3]}/energy) * ({params.B[3]} + {params.C[3]}/energy)",
+                                f"{params.A[4]} * (energy**({params.B[4]}-1)) * np.exp(-{params.C[4]}/energy) * ({params.B[4]} + {params.C[4]}/energy)",
+                                f"{params.A[5]} * (energy**({params.B[5]}-1)) * np.exp(-{params.C[5]}/energy) * ({params.B[5]} + {params.C[5]}/energy)",
+                                f"{params.A[6]} * (energy**({params.B[6]}-1)) * np.exp(-{params.C[6]}/energy) * ({params.B[6]} + {params.C[6]}/energy)",
+				f"{params.A[7]} * (energy**({params.B[7]}-1)) * np.exp(-{params.C[7]}/energy) * ({params.B[7]} + {params.C[7]}/energy)",
+		                f"{params.A[8]} * (energy**({params.B[8]}-1)) * np.exp(-{params.C[8]}/energy) * ({params.B[8]} + {params.C[8]}/energy)",
+                                f"{params.A[9]} * (energy**({params.B[9]}-1)) * np.exp(-{params.C[9]}/energy) * ({params.B[9]} + {params.C[9]}/energy)",
+                                f"{params.A[10]} * (energy**({params.B[10]}-1)) * np.exp(-{params.C[10]}/energy) * ({params.B[10]} + {params.C[10]}/energy)",
+                                f"{params.A[11]} * (energy**({params.B[11]}-1)) * np.exp(-{params.C[11]}/energy) * ({params.B[11]} + {params.C[11]}/energy)",
+                                f"{params.A[12]} * (energy**({params.B[12]}-1)) * np.exp(-{params.C[12]}/energy) * ({params.B[12]} + {params.C[12]}/energy)",
+                                f"{params.A[13]} * (energy**({params.B[13]}-1)) * np.exp(-{params.C[13]}/energy) * ({params.B[13]} + {params.C[13]}/energy)",
+                                f"{params.A[14]} * (energy**({params.B[14]}-1)) * np.exp(-{params.C[14]}/energy) * ({params.B[14]} + {params.C[14]}/energy)",
+                                f"{params.A[15]} * (energy**({params.B[15]}-1)) * np.exp(-{params.C[15]}/energy) * ({params.B[15]} + {params.C[15]}/energy)",
+                                f"{params.A[16]} * (energy**({params.B[16]}-1)) * np.exp(-{params.C[16]}/energy) * ({params.B[16]} + {params.C[16]}/energy)",
+                                f"{params.A[17]} * (energy**({params.B[17]}-1)) * np.exp(-{params.C[17]}/energy) * ({params.B[17]} + {params.C[17]}/energy)",
+                                f"{params.A[18]} * (energy**({params.B[18]}-1)) * np.exp(-{params.C[18]}/energy) * ({params.B[18]} + {params.C[18]}/energy)",
+                                f"{params.A[19]} * (energy**({params.B[19]}-1)) * np.exp(-{params.C[19]}/energy) * ({params.B[19]} + {params.C[19]}/energy)",
+                                f"{params.A[20]} * (energy**({params.B[20]}-1)) * np.exp(-{params.C[20]}/energy) * ({params.B[20]} + {params.C[20]}/energy)",
+                                f"{params.A[21]} * (energy**({params.B[21]}-1)) * np.exp(-{params.C[21]}/energy) * ({params.B[21]} + {params.C[21]}/energy)",
+                                f"{params.A[22]} * (energy**({params.B[22]}-1)) * np.exp(-{params.C[22]}/energy) * ({params.B[22]} + {params.C[22]}/energy)",
+                                f"{params.A[23]} * (energy**({params.B[23]}-1)) * np.exp(-{params.C[23]}/energy) * ({params.B[23]} + {params.C[23]}/energy)",
+                                f"{params.A[24]} * (energy**({params.B[24]}-1)) * np.exp(-{params.C[24]}/energy) * ({params.B[24]} + {params.C[24]}/energy)",
+                                f"{params.A[25]} * (energy**({params.B[25]}-1)) * np.exp(-{params.C[25]}/energy) * ({params.B[25]} + {params.C[25]}/energy)",
+                                f"{params.A[26]} * (energy**({params.B[26]}-1)) * np.exp(-{params.C[26]}/energy) * ({params.B[26]} + {params.C[26]}/energy)",
+                                f"{params.A[27]} * (energy**({params.B[27]}-1)) * np.exp(-{params.C[27]}/energy) * ({params.B[27]} + {params.C[27]}/energy)",
+                                f"{params.A[28]} * (energy**({params.B[28]}-1)) * np.exp(-{params.C[28]}/energy) * ({params.B[28]} + {params.C[28]}/energy)",
+                                f"{params.A[29]} * (energy**({params.B[29]}-1)) * np.exp(-{params.C[29]}/energy) * ({params.B[29]} + {params.C[29]}/energy)",
+                                f"{params.A[30]} * (energy**({params.B[30]}-1)) * np.exp(-{params.C[30]}/energy) * ({params.B[30]} + {params.C[30]}/energy)",
+                                f"{params.A[31]} * (energy**({params.B[31]}-1)) * np.exp(-{params.C[31]}/energy) * ({params.B[31]} + {params.C[31]}/energy)",
+                                f"{params.A[32]} * (energy**({params.B[32]}-1)) * np.exp(-{params.C[32]}/energy) * ({params.B[32]} + {params.C[32]}/energy)",
+                                f"{params.A[33]} * (energy**({params.B[33]}-1)) * np.exp(-{params.C[33]}/energy) * ({params.B[33]} + {params.C[33]}/energy)"]
+           
+
+    reactionExpressionTypelist =  np.array([False,False,False,False,False,False,False,False, # Rxns 1-8
+                                           True,True,True,True,True,True,True,True,True,True,True,True,True,True, # Rxns 9-21
+                                           False,False,True,True,True,True,False,False,False,False,True,True]) # Rxns 22-34
+
+    thresholded_rxn = np.array([False, False, False, False, False, False, False, False,
+                                True, True, True, True, False, True, True, True, True, False, False, False, False,
+                                True, False, False, False, False, False, False, False, False, False, False, True,
+                                False])
+    reactionsList = []
+    LOGFilename = 'interpolationSample.log'
+    f = open(LOGFilename, 'w')
+
+    for i in range(Nr):
+        if reactionExpressionTypelist[i]:
+            if i < 14 or i == 16 or i == 19 or i > 23:
+                f = h5.File("../../../BOLSIGChemistry_NominalRates/{0:s}.h5".format(rxnNameDict[i]), 'r')
+                dataset = f["table"]
+            else:
+                f = h5.File("../../../BOLSIGChemistry_NominalRates/StepExcitation.h5", 'r')
+                dataset = f[rxnNameDict[i]]
+
+            Te = dataset[:,0]
+            Te /= 11604
+            rateCoeff = dataset[:,1]
+            if i > 23 and i < 28:
+                rateCoeff /= 6.022e23**2
+            else:
+                rateCoeff /= 6.022e23
+
+            ## Delete BOLSIG failures
+            fail_inds = []
+            for j in range(len(rateCoeff)):
+                if rateCoeff[j] == 0.0 and j > np.nonzero(rateCoeff)[0][0]:
+                    fail_inds.append(j)
+
+            rateCoeff = np.delete(rateCoeff, fail_inds)
+            Te = np.delete(Te, fail_inds)
+
+            # Sorting mean energy array and rate coefficient array based on
+            # the mean energy array.
+            Teinds = Te.argsort()
+            rateCoeff = rateCoeff[Teinds]
+            Te = Te[Teinds]
+
+            # Find duplicates
+            TeDuplicateinds = np.where(np.abs(np.diff(Te, axis=0)) > 0.0)
+            TeDuplicateindsForLog = np.where(np.abs(np.diff(Te, axis=0)) == 0.0)
+            rateCoeff = rateCoeff[TeDuplicateinds]
+            Te = Te[TeDuplicateinds]
+
+            # Nondimensionalization of mean energy.
+            Te *= 1.5
+
+            # Find first non-zero value of the coefficient rate.
+            I = np.nonzero(rateCoeff)
+
+            diffRateCoeff = [j-i for i, j in zip(rateCoeff[:-1], rateCoeff[1:])]
+            diffTe = [j-i for i, j in zip(Te[:-1], Te[1:])]
+
+            Monotonicity = np.asarray([j/i for i, j in zip(diffTe, diffRateCoeff)])
+            Monotonicity = np.insert(Monotonicity, 0, 0.0, axis=0)
+
+            Nan = np.isnan(Monotonicity)
+            Inf = np.isinf(Monotonicity)
+            if thresholded_rxn[i] == True:
+                indexPositive = np.where(Monotonicity>0.0)
+            else:
+                indexPositive = np.where(Monotonicity<0.0)
+            Positive = np.full(Monotonicity.shape, False, dtype=bool)
+            Positive[indexPositive] = True
+            
+            indices = Nan + Inf + Positive
+
+
+            #lastFalse = np.where(indices==False)[-1][-1] + 2
+            for k in range(len(Te)):
+               if Te[k] < 4.5 and indices[k] == False:
+                  lastFalse = k + 2
+            
+            # Transformation to log scale.
+            TeLog = np.log(Te)
+
+            # Compute the slope of the rate coefficient between its first two non-zero values.
+            # Finite differences are used.
+            dydx = (rateCoeff[lastFalse + 1] - rateCoeff[lastFalse]) \
+                 / (Te[lastFalse + 1] - Te[lastFalse])
+
+            # Arrhenius form: kf = A * exp(-C / Te)
+            C = Te[lastFalse]**2.0*dydx / rateCoeff[lastFalse]
+
+            # Compute pre-exponential coefficient, A, in log scale.
+            ALog = np.log(rateCoeff[lastFalse]) + C / Te[lastFalse]
+
+            # Transform rate coefficient in log scale.
+            rateCoeffLog = np.zeros(rateCoeff.shape)
+            rateCoeffLog[lastFalse:] = np.log(rateCoeff[lastFalse:])
+            # For the troublesome values, we use the Arrhenius form.
+            rateCoeffLog[0:lastFalse] = ALog - C / Te[0:lastFalse]
+            # Nondimensionalization in log scale.
+            if (i < 12):
+                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
+            elif i > 23 and i < 28:
+                rateCoeffLog += - np.log(1.0/tau) + 2*np.log(np0)
+            else:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(np0)
+
+            # Interpolation in log scale.
+            reactionExpressionsLog = CubicSpline(TeLog, rateCoeffLog)
+            # Gradient in log scale
+            reactionTExpressionsLog = CubicSpline.derivative(reactionExpressionsLog)
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf_log = reactionExpressionsLog,
+                                kf_T_log = reactionTExpressionsLog)
+            reactionsList.append(reaction)
+
+            logging.basicConfig(filename=LOGFilename)
+            logging.warning('Interpolation info (Reaction %s):', i + 1)
+            logging.warning('First non-zero entry in the rate coefficient: %s', I[0][0])
+            logging.warning('Monotonicity of the rate coefficient start from entry: %s', lastFalse)
+            logging.warning('Position of possible duplicates in mean energy array: %s',
+                          TeDuplicateindsForLog[0])
+
+        else:
+            rxn   = eval("lambda energy :" + reactionExpressionslist[i])
+            rxn_T = eval("lambda energy :" + reactionTExpressionslist[i])
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf = rxn, kf_T = rxn_T)
+            reactionsList.append(reaction)
+
+    params.reactionsList = reactionsList
+
+    ## Electron Transport Data
+    diffList = []
+    transport = h5.File("../../../BOLSIGChemistry_NominalRates/nominal_transport.h5", 'r')
+    NDe_v_Te = transport["diffusivity"]
+    Te_trans = NDe_v_Te[:,0]
+    Te_trans /= 11604
+    De_interp = (NDe_v_Te[:,1]/nAr)*tau/(L*L)
+    De_spline = CubicSpline(Te_trans, De_interp)
+    De_Te_spline = CubicSpline.derivative(De_spline)
+    diffusivity = Diffusivity(interpolate = True, D_expression = De_spline, D_T_expression = De_Te_spline)
+    diffList.append(diffusivity)
+
+    Ns = 6
+    for i in range(1, Ns):
+        diffList.append(Diffusivity(interpolate = False))
+
+    params.diffusivityList = diffList
+
+    muList = []
+    Nmue_v_Te = transport["mobility"]
+    mue_interp = (Nmue_v_Te[:,1]/nAr)*V0*tau/(L*L)
+    mue_spline = CubicSpline(Te_trans, mue_interp)
+    mue_Te_spline = CubicSpline.derivative(mue_spline)
+    mobility = Mobility(interpolate = True, mu_expression = mue_spline, mu_T_expression = mue_Te_spline)
+    muList.append(mobility)
+
+    Ns = 6
+    for i in range(1, Ns):
+        muList.append(Mobility(interpolate = False))
+
+    params.mobilityList = muList
+    
+    # 5) Dump to screen
+    params.print()
diff --git a/psaapProperties_6Species_500mTorr.py b/psaapProperties_6Species_500mTorr.py
new file mode 100755
index 000000000..99fa3b14d
--- /dev/null
+++ b/psaapProperties_6Species_500mTorr.py
@@ -0,0 +1,533 @@
+import numpy as np
+from scipy.interpolate import CubicSpline
+import csv
+#import matplotlib.pyplot as plt
+#import matplotlib.colors as mcolors
+import h5py as h5
+
+import logging
+
+class Reaction(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Diffusivity(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Mobility(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+
+def setPsaapProperties_6Species_500mTorr(gam, inputV0, inputVDC, params, Nr, iSample):
+    """Sets non-dimensional properties corresponding to Liu 2014 paper.
+
+    Inputs:
+      gam : Secondary electron emission coefficient
+      params : chebSolver.modelParams class
+
+    Outputs: None
+      params data is overwritten using values from Liu 2014.
+    """
+    ###################################################################
+    # User specified parameters (you may change these if you wish to
+    # run a different scenario from Liu 2014)
+    ###################################################################
+
+    # densities
+    nAr = 1.61e22     # background number density of Ar [1/m^3] (corresponds to p=100 mTorr)
+    np0 = 8e16        # "nominal" electron density [1/m^3]
+
+    # masses
+    # me = 9.10938356e-31        # mass of an electron [kg]
+    # me = 5.489e-4              # mass of an electron [u]
+    me = 0.511e6                 # mass of an electron [eV/c2]
+    # mAr = 39.948               # mass of an argon atom [u]
+    # mAr = 39.948 * 1.66054e-27 # mass of an argon atom [kg]
+    mAr = 37.2158e9              # mass of an argon atom [eV/c2]
+    # u = 931.4941e6             # eV/c2
+    c = 299792458                # speed of light [m/s]
+    se = 40                      # momentum cross section [A^2]
+
+    # nominal electron energy
+    e0 = 1.0  # [eV]
+
+    # pressure
+    p  = 66.6*1.5      # [J/m^3] *1.5 to convert it to energy (1 Torr)
+
+    # gas energy at the wall
+    Tg0 = 0.038778    # 3/2*300K*kB ~ (p0 - nT[:,0])/ntot
+
+    # characteristics of driving voltage
+    V0  = inputV0                 # amplitude of driving voltage [V]
+    verticalShift = inputVDC      # DC voltage (vertical shift in driving voltage)
+    tau = (1./13.56e6)             # period of driving voltage [s]
+    L   = 2.00*0.005              # half-gap-width [m] (gap width is 2 cm)
+    electrodeArea = np.pi*0.05**2 # electrode area [m^2] (electrode diameter = 0.1 m)
+
+    # transport parameters
+    nmue = 9.66e21   # argon number density times electron mobility [1/(V*cm*s)]
+    nmum = 0.0
+    nmur = 0.0
+    nmu4p = 0.0
+    nmui = 8.0e19
+    nDe  = 3.86e22   # argon number density times electron diffusivity [1/(cm*s)]
+    nDi  = 2.07e18   # argon number density times ion diffusivity [1/(cm*s)]
+    nDm  = 2.42e18   # argon number density times AR(m) diffusivity [1/(cm*s)]
+    nDr  = 2.42e18
+    nD4p = 2.42e18
+
+    # reaction parameters (NB: k_i = Ck*Ee^B*exp(-A/Ee))
+    #                          Ee = 3/2*Te (Te in eV)
+    #                          -> k_i = [Ck*(2/3)^B] * Ee^B * exp[-(3/2)*A/Ee]
+    # nominal
+    Ck = np.array([2.0e-7,2.1e-9,5.0e-10,6.4e-10,2.1e-15,1.32e8,0.0,3.0e7,3.0e7,0.0,0.0,0.0,0.0,4.3e-10,0.0,3.7e-8,8.9e-7,1.8e-7,3.0e-7,3.0e-7,4.3e-10,9.1e-7,8.9e-7]) # pre-exponential factors [cm^3/s]
+    B  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0.74,0,0,0.51,0.61,0.51,0.51,0.74,0,0.51])
+    A  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.59,2.61,0,0,0,0,1.59]) # activation temperature [eV]
+    dH = np.array([0.0,-7.412,-10.054,-7.336,0.0,0.0,0.0,0.0,0.0,11.548,11.624,12.907,15.76,-11.548,4.212,0.076,1.359,2.853,-1.283,-1.359,-11.624,-0.076,0.983]) # energy lost per electron due to ionization rxn [eV]
+    dEps = np.array([0.0,15.76,11.548,11.624,12.907,0.0]) # E, AR+, AR(m), AR(r), AR(4p), AR
+
+    # BC parameters
+    # ks = 1.19e7  # electron recombination rate [cm/s]
+    ks = 1.366109824889323e7 # electron recombination rate [cm/s/eV]
+
+    ###################################################################
+    # Constants of nature (probably shouldn't change unless you have
+    # root privileges on universe)
+    ###################################################################
+    qe   = 1.6e-19   # unit charge [C]
+    eps0 = 8.86e-12  # permittivity of free space [F/m]
+    kB   = 1.38e-23  # Boltzmann constant [J/K]
+    # kB   = 8.62e-5 # Boltzmann constant [eV/K]
+
+
+    ###################################################################
+    # Calculate non-dimensional parameters
+    ###################################################################
+
+    # 1) Convert input units to base SI (except eV)
+    nDe  *= 100. # 1/(m*s)
+    nDi  *= 100. # 1/(m*s)
+    nDm  *= 100.
+    nDr  *= 100.
+    nD4p *= 100.
+    nmue *= 100. # 1/(V*m*s)
+    nmui *= 100. # 1/(V*m*s)
+    Ck[0:5] *= 1e-6 # m^3/s
+    Ck[5:9] *= 1 # 1/s
+    Ck[9:]  *= 1e-6 # m^3/s
+    ks   *= 0.01 # m/s
+    se   *= 1.0e-20  # m^2
+
+    # 2) Compute "raw" transport parameters
+    De  = nDe/nAr
+    Di  = nDi/nAr
+    Dm  = nDm/nAr
+    Dr  = nDr/nAr
+    D4p = nD4p/nAr
+
+    mue = nmue/nAr
+    mui = nmui/nAr
+    mum = nmum/nAr
+    mur = nmur/nAr
+    mu4p = nmu4p/nAr
+
+    # 3) Compute non-dimensional properties required by solver
+    De    = De*tau/(L*L)
+    Di    = Di*tau/(L*L)
+    Dm    = Dm*tau/(L*L)
+    Dr    = Dr*tau/(L*L)
+    D4p   = D4p*tau/(L*L)
+
+    mue   = mue*V0*tau/(L*L)
+    mui   = mui*V0*tau/(L*L)
+    mum   = mum*V0*tau/(L*L)
+    mur   = mur*V0*tau/(L*L)
+    mu4p  = mu4p*V0*tau/(L*L)
+
+    Ck[0:4]  *= tau*np0
+    Ck[4]    *= tau*nAr
+    Ck[5:9]  *= tau
+    Ck[9:13] *= tau*nAr
+    Ck[13:]  *= tau*np0
+    A        = A*1.5/e0  # 1.5 to convert from temperature to energy
+    dH       = dH/e0
+    qStar    = V0/e0 # qe*V0/e0, since e0 in eV, need qe*V0 in eV, which is just V0 in V
+    alpha    = qe*np0*L*L/(V0*eps0)
+    ks       = ks*tau/L
+    p0       = p/qe/np0
+    kappaB   = 4.878171165833662*1.6129 # non-dimensional thermal conductivity of background specie
+                                # (2/3)*tau/L**2*Kb/np0/kB,
+                                # where Kb is the thermal conductivity of background specie
+
+    params.beta = np.array([[0,1,1,1,0,0,0,0,0,1,1,1,2,1,2,1,1,2,1,1,1,1,1],                     # E
+                            [0,1,1,1,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0],                     # AR+
+                            [0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0],                     # AR(m)
+                            [0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0],                     # AR(r)
+                            [0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1],                     # AR(4p)
+                            [2,1,1,1,2,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0]], dtype=np.int64)    # AR
+
+    params.alfa = np.array([[0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1],                     # E
+                            [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],                     # AR+
+                            [2,1,0,2,1,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0],                     # AR(m)
+                            [0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1],                     # AR(r)
+                            [0,0,2,0,0,0,1,1,1,0,0,0,0,0,0,0,0,1,1,1,0,0,0],                     # AR(4p)
+                            [0,0,0,0,1,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0]], dtype=np.int64)    # AR
+	# Rxn1:  2AR(m)         ->   2AR
+	# Rxn2:  AR(m) + AR(r)  ->   E + AR+ + AR
+	# Rxn3:  2AR(4p)        ->   E + AR+ + AR
+	# Rxn4:  2AR(m)         ->   E + AR+ + AR
+	# Rxn5:  AR(m) + AR     ->   2AR
+	# Rxn6:  AR(r)          ->   AR
+	# Rxn7:  AR(4p)         ->   AR
+	# Rxn8:  AR(4p)         ->   AR(m)
+	# Rxn9:  AR(4p)         ->   AR(r)
+    # Rxn10: E + AR         ->   E + AR(m)
+    # Rxn11: E + AR         ->   E + AR(r)
+    # Rxn12: E + AR         ->   E + AR(4p)
+    # Rxn13: E + AR         ->   2E + AR+
+    # Rxn14: E + AR(m)      ->   E + AR
+    # Rxn15: E + AR(m)      ->   2E + AR+
+    # Rxn16: E + AR(m)      ->   E + AR(r)
+    # Rxn17: E + AR(m)      ->   E + AR(4p)
+    # Rxn18: E + AR(4p)     ->   2E + AR+
+    # Rxn19: E + AR(4p)     ->   E + AR(r)
+    # Rxn20: E + AR(4p)     ->   E + AR(m)
+    # Rxn21: E + AR(r)      ->   E + AR
+    # Rxn22: E + AR(r)      ->   E + AR(m)
+    # Rxn23: E + AR(r)      ->   E + AR(4p)
+
+    rxnNameDict = { 0: "2Ar(m) => 2Ar",
+                    1: "Ar(m) + Ar(r) => E + Ar+ + Ar",
+                    2: "2Ar(4p) => E + Ar+ + Ar",
+                    3: "2Ar(m) => E + Ar+ + Ar",
+                    4: "Ar(m) + Ar => 2Ar",
+                    5: "Ar(r) => Ar",
+                    6: "Ar(4p) => Ar",
+                    7: "Ar(4p) => Ar(m)",
+                    8: "Ar(4p) => Ar(r)",
+                    9: "lumped.metastable",
+                   10: "lumped.resonance",
+                   11: "lumped.2p",
+                   12: "ionization",
+                   13: "E + Ar(m) => E + Ar",
+                   14: "step_ionization",
+                   15: "E + Ar(m) => E + Ar(r)",
+                   16: "E + Ar(m) => E + Ar(4p)",
+                   17: "E + Ar(4p) => 2E + Ar+",
+                   18: "E + Ar(4p) => E + Ar(r)",
+                   19: "E + Ar(4p) => E + Ar(m)",
+                   20: "E + Ar(r) => E + Ar",
+                   21: "E + Ar(r) => E + Ar(m)",
+                   22: "E + Ar(r) => E + Ar(4p)"}
+
+    # 4) Set values in params class
+    params.D[0]    = De
+    params.D[1]    = Di
+    params.D[2]    = Dm
+    params.D[3]    = Dr
+    params.D[4]    = D4p
+
+    params.mu[0]   = mue
+    params.mu[1]   = mui
+    params.mu[2]   = mum
+    params.mu[3]   = mur
+    params.mu[4]   = mu4p
+
+    params.A[:]    = Ck[:]
+    params.B[:]    = B[:]
+    params.C[:]    = A[:]
+
+    # Account for the 2/3 term to convert from electron temperature to electron energy
+    for i in range(len(params.A)):
+        params.A[i] *= (2/3)**(params.B[i])
+
+    params.dH[:]   = dH[:]
+    params.dEps[:] = dEps[:]
+    params.qStar   = qStar
+    params.alpha   = alpha
+    params.ks      = ks
+    params.gam     = gam
+    params.kappaB  = kappaB
+    params.nAronp0 = nAr / np0
+    params.p0      = p0
+    params.Tg0     = Tg0
+    params.EC      = 2.0 * me / mAr \
+        * np.sqrt(16.0 * (me + mAr) * e0 * c**2
+                  / (3.0 * np.pi * me * mAr)) * se * nAr * tau
+    # params.EC = 2.0 * me / mAr * 3.8e9 * tau
+
+    params.verticalShift = verticalShift / V0
+
+    # Parameters needed to compute the current with dimensions
+    params.V0Ltau  = V0 / (L * tau)
+    params.V0L     = V0 / L
+    params.LLV0tau = (L*L) / (V0*tau)
+    params.tauL    = L / tau
+    params.np0     = np0           # "nominal" electron density [1/m^3]
+    params.qe      = qe            # unit charge [C]
+    params.eps0    = eps0          # unit charge [C]
+    params.eArea   = electrodeArea # electrode area [m^2]
+
+
+    reactionExpressionslist = [f"{params.A[0]} * energy**{params.B[0]} * np.exp(-{params.C[0]} / energy)",
+                               f"{params.A[1]} * energy**{params.B[1]} * np.exp(-{params.C[1]} / energy)",
+                               f"{params.A[2]} * energy**{params.B[2]} * np.exp(-{params.C[2]} / energy)",
+                               f"{params.A[3]} * energy**{params.B[3]} * np.exp(-{params.C[3]} / energy)",
+                               f"{params.A[4]} * energy**{params.B[4]} * np.exp(-{params.C[4]} / energy)",
+                               f"{params.A[5]} * energy**{params.B[5]} * np.exp(-{params.C[5]} / energy)",
+                               f"{params.A[6]} * energy**{params.B[6]} * np.exp(-{params.C[6]} / energy)",
+                               f"{params.A[7]} * energy**{params.B[7]} * np.exp(-{params.C[7]} / energy)",
+                               f"{params.A[8]} * energy**{params.B[8]} * np.exp(-{params.C[8]} / energy)",
+                               f"{params.A[9]} * energy**{params.B[9]} * np.exp(-{params.C[9]} / energy)",
+                               f"{params.A[10]} * energy**{params.B[10]} * np.exp(-{params.C[10]} / energy)",
+                               f"{params.A[11]} * energy**{params.B[11]} * np.exp(-{params.C[11]} / energy)",
+                               f"{params.A[12]} * energy**{params.B[12]} * np.exp(-{params.C[12]} / energy)",
+                               f"{params.A[13]} * energy**{params.B[13]} * np.exp(-{params.C[13]} / energy)",
+                               f"{params.A[14]} * energy**{params.B[14]} * np.exp(-{params.C[14]} / energy)",
+                               f"{params.A[15]} * energy**{params.B[15]} * np.exp(-{params.C[15]} / energy)",
+                               f"{params.A[16]} * energy**{params.B[16]} * np.exp(-{params.C[16]} / energy)",
+                               f"{params.A[17]} * energy**{params.B[17]} * np.exp(-{params.C[17]} / energy)",
+                               f"{params.A[18]} * energy**{params.B[18]} * np.exp(-{params.C[18]} / energy)",
+                               f"{params.A[19]} * energy**{params.B[19]} * np.exp(-{params.C[19]} / energy)",
+                               f"{params.A[20]} * energy**{params.B[20]} * np.exp(-{params.C[20]} / energy)",
+                               f"{params.A[21]} * energy**{params.B[21]} * np.exp(-{params.C[21]} / energy)",
+                               f"{params.A[22]} * energy**{params.B[22]} * np.exp(-{params.C[22]} / energy)"]
+
+    reactionTExpressionslist = [f"{params.A[0]} * (energy**({params.B[0]}-1)) * np.exp(-{params.C[0]}/energy) * ({params.B[0]} + {params.C[0]}/energy)",
+                                f"{params.A[1]} * (energy**({params.B[1]}-1)) * np.exp(-{params.C[1]}/energy) * ({params.B[1]} + {params.C[1]}/energy)",
+                                f"{params.A[2]} * (energy**({params.B[2]}-1)) * np.exp(-{params.C[2]}/energy) * ({params.B[2]} + {params.C[2]}/energy)",
+                                f"{params.A[3]} * (energy**({params.B[3]}-1)) * np.exp(-{params.C[3]}/energy) * ({params.B[3]} + {params.C[3]}/energy)",
+                                f"{params.A[4]} * (energy**({params.B[4]}-1)) * np.exp(-{params.C[4]}/energy) * ({params.B[4]} + {params.C[4]}/energy)",
+                                f"{params.A[5]} * (energy**({params.B[5]}-1)) * np.exp(-{params.C[5]}/energy) * ({params.B[5]} + {params.C[5]}/energy)",
+                                f"{params.A[6]} * (energy**({params.B[6]}-1)) * np.exp(-{params.C[6]}/energy) * ({params.B[6]} + {params.C[6]}/energy)",
+				f"{params.A[7]} * (energy**({params.B[7]}-1)) * np.exp(-{params.C[7]}/energy) * ({params.B[7]} + {params.C[7]}/energy)",
+		                f"{params.A[8]} * (energy**({params.B[8]}-1)) * np.exp(-{params.C[8]}/energy) * ({params.B[8]} + {params.C[8]}/energy)",
+                                f"{params.A[9]} * (energy**({params.B[9]}-1)) * np.exp(-{params.C[9]}/energy) * ({params.B[9]} + {params.C[9]}/energy)",
+                                f"{params.A[10]} * (energy**({params.B[10]}-1)) * np.exp(-{params.C[10]}/energy) * ({params.B[10]} + {params.C[10]}/energy)",
+                                f"{params.A[11]} * (energy**({params.B[11]}-1)) * np.exp(-{params.C[11]}/energy) * ({params.B[11]} + {params.C[11]}/energy)",
+                                f"{params.A[12]} * (energy**({params.B[12]}-1)) * np.exp(-{params.C[12]}/energy) * ({params.B[12]} + {params.C[12]}/energy)",
+                                f"{params.A[13]} * (energy**({params.B[13]}-1)) * np.exp(-{params.C[13]}/energy) * ({params.B[13]} + {params.C[13]}/energy)",
+                                f"{params.A[14]} * (energy**({params.B[14]}-1)) * np.exp(-{params.C[14]}/energy) * ({params.B[14]} + {params.C[14]}/energy)",
+                                f"{params.A[15]} * (energy**({params.B[15]}-1)) * np.exp(-{params.C[15]}/energy) * ({params.B[15]} + {params.C[15]}/energy)",
+                                f"{params.A[16]} * (energy**({params.B[16]}-1)) * np.exp(-{params.C[16]}/energy) * ({params.B[16]} + {params.C[16]}/energy)",
+                                f"{params.A[17]} * (energy**({params.B[17]}-1)) * np.exp(-{params.C[17]}/energy) * ({params.B[17]} + {params.C[17]}/energy)",
+                                f"{params.A[18]} * (energy**({params.B[18]}-1)) * np.exp(-{params.C[18]}/energy) * ({params.B[18]} + {params.C[18]}/energy)",
+                                f"{params.A[19]} * (energy**({params.B[19]}-1)) * np.exp(-{params.C[19]}/energy) * ({params.B[19]} + {params.C[19]}/energy)",
+                                f"{params.A[20]} * (energy**({params.B[20]}-1)) * np.exp(-{params.C[20]}/energy) * ({params.B[20]} + {params.C[20]}/energy)",
+                                f"{params.A[21]} * (energy**({params.B[21]}-1)) * np.exp(-{params.C[21]}/energy) * ({params.B[21]} + {params.C[21]}/energy)",
+                                f"{params.A[22]} * (energy**({params.B[22]}-1)) * np.exp(-{params.C[22]}/energy) * ({params.B[22]} + {params.C[22]}/energy)"]
+           
+
+    reactionExpressionTypelist =  np.array([False,False,False,False,False,False,False,False,False,
+                                           True,True,True,True,False,True,True,True,False,True,True,False,True,True])
+
+    reactionsList = []
+    LOGFilename = 'interpolationSample.log'
+    f = open(LOGFilename, 'w')
+
+    for i in range(Nr):
+        if reactionExpressionTypelist[i]:
+            if (i < 15):
+                f = h5.File("../BOLSIGChemistry_6SpeciesRates/{0:s}.h5".format(rxnNameDict[i]), 'r')
+                dataset = f["table"]
+            else:
+                f = h5.File("../BOLSIGChemistry_6SpeciesRates/StepwiseExcitations.nominal.h5", 'r')
+                dataset = f[rxnNameDict[i]]
+
+            Te = dataset[:,0]
+            Te /= 11604
+            rateCoeff = dataset[:,1]
+            rateCoeff /= 6.022e23
+
+            # Sorting mean energy array and rate coefficient array based on
+            # the mean energy array.
+            Teinds = Te.argsort()
+            rateCoeff = rateCoeff[Teinds]
+            Te = Te[Teinds]
+
+            # Find duplicates
+            TeDuplicateinds = np.where(np.abs(np.diff(Te, axis=0)) > 0.0)
+            TeDuplicateindsForLog = np.where(np.abs(np.diff(Te, axis=0)) == 0.0)
+            rateCoeff = rateCoeff[TeDuplicateinds]
+            Te = Te[TeDuplicateinds]
+
+            # Nondimensionalization of mean energy.
+            Te *= 1.5
+
+            # Find first non-zero value of the coefficient rate.
+            I = np.nonzero(rateCoeff)
+
+            diffRateCoeff = [j-i for i, j in zip(rateCoeff[:-1], rateCoeff[1:])]
+            diffTe = [j-i for i, j in zip(Te[:-1], Te[1:])]
+
+            Monotonicity = np.asarray([j/i for i, j in zip(diffTe, diffRateCoeff)])
+            Monotonicity = np.insert(Monotonicity, 0, 0.0, axis=0)
+
+            Nan = np.isnan(Monotonicity)
+            Inf = np.isinf(Monotonicity)
+            indexPositive = np.where(Monotonicity>0.0)
+            Positive = np.full(Monotonicity.shape, False, dtype=bool)
+            Positive[indexPositive] = True
+            
+            indices = Nan + Inf + Positive
+
+
+            #lastFalse = np.where(indices==False)[-1][-1] + 2
+            for k in range(len(Te)):
+               if Te[k] < 4.5 and indices[k] == False:
+                  lastFalse = k + 2
+
+            # Transformation to log scale.
+            TeLog = np.log(Te)
+
+            # Compute the slope of the rate coefficient between its first two non-zero values.
+            # Finite differences are used.
+            dydx = (rateCoeff[lastFalse + 1] - rateCoeff[lastFalse]) \
+                 / (Te[lastFalse + 1] - Te[lastFalse])
+
+            # Arrhenius form: kf = A * exp(-C / Te)
+            C = Te[lastFalse]**2.0*dydx / rateCoeff[lastFalse]
+
+            # Compute pre-exponential coefficient, A, in log scale.
+            ALog = np.log(rateCoeff[lastFalse]) + C / Te[lastFalse]
+
+            # Transform rate coefficient in log scale.
+            rateCoeffLog = np.zeros(rateCoeff.shape)
+            rateCoeffLog[lastFalse:] = np.log(rateCoeff[lastFalse:])
+            # For the troublesome values, we use the Arrhenius form.
+            rateCoeffLog[0:lastFalse] = ALog - C / Te[0:lastFalse]
+            # Nondimensionalization in log scale.
+            if (i < 13):
+                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
+            else:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(np0)
+
+            # Interpolation in log scale.
+            reactionExpressionsLog = CubicSpline(TeLog, rateCoeffLog)
+            # Gradient in log scale
+            reactionTExpressionsLog = CubicSpline.derivative(reactionExpressionsLog)
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf_log = reactionExpressionsLog,
+                                kf_T_log = reactionTExpressionsLog)
+            reactionsList.append(reaction)
+
+            logging.basicConfig(filename=LOGFilename)
+            logging.warning('Interpolation info (Reaction %s):', i + 1)
+            logging.warning('First non-zero entry in the rate coefficient: %s', I[0][0])
+            logging.warning('Monotonicity of the rate coefficient start from entry: %s', lastFalse)
+            logging.warning('Position of possible duplicates in mean energy array: %s',
+                          TeDuplicateindsForLog[0])
+
+        else:
+            rxn   = eval("lambda energy :" + reactionExpressionslist[i])
+            rxn_T = eval("lambda energy :" + reactionTExpressionslist[i])
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf = rxn, kf_T = rxn_T)
+            reactionsList.append(reaction)
+
+    params.reactionsList = reactionsList
+
+    diffList = []
+    # Data from BOLSIG
+    # Te in [eV]
+    # De * N in [1/(m*s)]
+    #NDe_v_Te = np.array([ [-0.05, 1.8e25],         [0.0, 1.8e25],           [0.05, 1.8e25],          [0.1, 1.8e25],
+    #                      [0.2103718, 1.8e25],     [0.2244455, 1.8e25],     [0.2390528, 1.8e25],     [0.2544605, 1.8e25],
+    #                      [0.2710021, 1.8e25],     [0.2886776, 1.8e25],     [0.3078205, 1.8e25],     [0.3286309, 1.8e25],
+    #                      [0.3513089, 1.8e25],     [0.3760546, 1.8e25],     [0.4032682, 1.8e25],     [0.4331498, 1.8e25],
+    #                      [0.4659662, 1.8e25],     [0.5021843, 1.8e25],     [0.5424044, 1.8e25],     [0.5868266, 1.8e25],
+    #                      [0.6359845, 1.8e25],     [0.690345, 1.8e25],      [0.749041, 1.8e25],      [0.813073, 1.8e25],
+    #                      [1.217942, 1.46E+25],    [1.319326, 1.39E+25],    [1.430048, 1.32E+25],    [1.550108, 1.26E+25],
+    #                      [1.681507, 1.20E+25],    [1.823578, 1.15E+25],    [1.977655, 1.09E+25],    [2.143071, 1.05E+25],
+    #                      [2.319826, 9.98E+24],    [2.508587, 9.54E+24],    [2.709354, 9.13E+24],    [2.916124, 8.75E+24],
+    #                      [3.112889, 8.45E+24],    [3.272969, 8.24E+24],    [3.383691, 8.14E+24],    [3.456394, 8.09E+24],
+    #                      [3.505085, 8.07E+24],    [3.544438, 8.05E+24],    [3.579789, 8.03E+24],    [3.616474, 8.01E+24],
+    #                      [3.656494, 7.97E+24],    [3.701183, 7.93E+24],    [3.751875, 7.87E+24],    [3.807903, 7.81E+24],
+    #                      [3.871268, 7.75E+24],    [3.941303, 7.68E+24],    [4.018675, 7.61E+24],    [4.102717, 7.53E+24],
+    #                      [4.193429, 7.5E+24],     [4.290811, 7.5E+24],     [4.39553, 7.5E+24],      [4.507586, 7.5E+24],
+    #                      [5., 7.5E+24],           [6., 7.5E+24],           [7., 7.5E+24],           [8., 7.5E+24],
+    #                      [9., 7.5E+24],           [10., 7.5E+24],          [11., 7.5E+24],          [12., 7.5E+24]])
+
+    transport = h5.File("../BOLSIGChemistry_Transport/nominal_transport.h5", 'r')
+    NDe_v_Te = transport["diffusivity"]
+    Te_trans = NDe_v_Te[:,0]
+    Te_trans /= 11604
+    print("Te_min = {0:.6e}".format(NDe_v_Te[0,0]))
+    print("Te_max = {0:.6e}".format(NDe_v_Te[-1,0]))
+    De_interp = (NDe_v_Te[:,1]/nAr)*tau/(L*L)
+    De_spline = CubicSpline(Te_trans, De_interp)
+    De_Te_spline = CubicSpline.derivative(De_spline)
+    diffusivity = Diffusivity(interpolate = True, D_expression = De_spline, D_T_expression = De_Te_spline)
+    diffList.append(diffusivity)
+
+    Ns = 6
+    for i in range(1, Ns):
+        diffList.append(Diffusivity(interpolate = False))
+
+    params.diffusivityList = diffList
+
+    muList = []
+    # Data from BOLSIG
+    # Te in [eV]
+    # Mue * N in [1/(V*m*s)]
+    #Nmue_v_Te = np.array([[0.02846756, 1.289e+26], [0.02975487, 1.33e+26],   [0.03173586, 1.383e+26],  [0.03477738,1.448e+26],
+    #                      [0.03937968, 1.523e+26], [0.04614973, 1.604e+26],  [0.05561446, 1.679e+26],  [0.0681674,1.735e+26],
+    #                      [0.0835084, 1.749e+26],  [0.1008504, 1.718e+26],   [0.1187927, 1.639e+26],   [0.1363348,1.522e+26],
+    #                      [0.1528764, 1.386e+26],  [0.1682841, 1.245e+26],   [0.1826913, 1.108e+26],   [0.1965649,9.809e+25],
+    #                      [0.2103718, 8.662e+25],  [0.2244455, 7.641e+25],   [0.2390528, 6.74e+25],    [0.2544605,5.947e+25],
+    #                      [0.2710021, 5.249e+25],  [0.2886776, 4.636e+25],   [0.3078205, 4.097e+25],   [0.3286309,3.623e+25],
+    #                      [0.3513089, 3.206e+25],  [0.3760546, 2.839e+25],   [0.4032682, 2.517e+25],   [0.4331498,2.232e+25],
+    #                      [0.4659662, 1.981e+25],  [0.5021843, 1.76e+25],    [0.5424044, 1.565e+25],   [0.5868266,1.392e+25],
+    #                      [0.6359845, 1.239e+25],  [0.690345, 1.103e+25],    [0.749041,  9.8e+24],     [0.813073,8.699e+24],
+    #                      [0.882441, 7.711e+24],   [0.957145, 6.828e+24],    [1.037185,  6.04e+24],    [1.123895,5.344e+24],
+    #                      [1.217942, 4.729e+24],   [1.319326, 4.186e+24],    [1.430048,  3.707e+24],   [1.550108,3.283e+24],
+    #                      [1.681507, 2.907e+24],   [1.823578, 2.574e+24],    [1.977655,  2.278e+24],   [2.143071,2.015e+24],
+    #                      [2.319826, 1.779e+24],   [2.508587, 1.569e+24],    [2.709354,  1.384e+24],   [2.916124,1.228e+24],
+    #                      [3.112889, 1.115e+24],   [3.272969, 1.055e+24],    [3.383691,  1.038e+24],   [3.456394,1.044e+24],
+    #                      [3.505085, 1.054e+24],   [3.544438, 1.062e+24],    [3.579789,  1.064e+24],   [3.616474, 1.059e+24],
+    #                      [3.656494, 1.048e+24],   [3.701183, 1.033e+24],    [3.751875,  1.014e+24],   [3.807903, 9.928e+23],
+    #                      [3.871268, 9.697e+23],   [3.941303,  9.459e+23],   [4.018675, 9.22e+23],     [4.102717, 8.991e+23],
+    #                      [4.193429, 8.773e+23],   [4.290811,  8.57e+23],    [4.39553, 8.383e+23],     [4.507586, 8.21e+23],
+    #                      [4.627646, 8.051e+23],   [4.757711,  7.901e+23],   [4.899115, 7.757e+23],    [5.054526, 7.617e+23],
+    #                      [5.227946, 7.479e+23],   [5.423377,  7.339e+23],   [5.646822, 7.198e+23],    [5.905618, 7.056e+23],
+    #                      [6.20977, 6.91e+23],     [6.571284,  6.757e+23],   [7.01017, 6.607e+23],     [7.54377, 6.458e+23],
+    #                      [8.19743, 6.303e+23],    [9.01117,   6.155e+23],   [10.02501, 6e+23],        [11.30565, 5.843e+23],
+    #                      [12.89978, 5.677e+23],   [14.91412,  5.51e+23],    [17.41537, 5.326e+23],    [20.57028, 5.149e+23],
+    #                      [24.51225, 4.968e+23],   [29.45472,  4.784e+23],   [35.6845, 4.602e+23],     [43.58845, 4.421e+23],
+    #                      [53.86692, 4.269e+23],   [67.367,    4.134e+23],   [85.4427, 4.026e+23],     [100.0, 4.026e+23]])
+
+    Nmue_v_Te = transport["mobility"]
+    #Te = Nmue_v_Te[:,0]
+    #Te /= 11604
+    mue_interp = (Nmue_v_Te[:,1]/nAr)*V0*tau/(L*L)
+    mue_spline = CubicSpline(Te_trans, mue_interp)
+    mue_Te_spline = CubicSpline.derivative(mue_spline)
+    mobility = Mobility(interpolate = True, mu_expression = mue_spline, mu_T_expression = mue_Te_spline)
+    muList.append(mobility)
+
+    Ns = 6
+    for i in range(1, Ns):
+        muList.append(Mobility(interpolate = False))
+
+    params.mobilityList = muList
+    
+    # 5) Dump to screen
+    params.print()
diff --git a/psaapProperties_6Species_500mTorr_Expanded.py b/psaapProperties_6Species_500mTorr_Expanded.py
new file mode 100755
index 000000000..0b4f723a6
--- /dev/null
+++ b/psaapProperties_6Species_500mTorr_Expanded.py
@@ -0,0 +1,549 @@
+import numpy as np
+from scipy.interpolate import CubicSpline
+import csv
+#import matplotlib.pyplot as plt
+#import matplotlib.colors as mcolors
+import h5py as h5
+
+import logging
+
+class Reaction(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Diffusivity(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Mobility(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+
+def setPsaapProperties_6Species_500mTorr_Expanded(gam, inputV0, inputVDC, params, Nr, iSample):
+    """Sets non-dimensional properties corresponding to Liu 2014 paper.
+
+    Inputs:
+      gam : Secondary electron emission coefficient
+      params : chebSolver.modelParams class
+
+    Outputs: None
+      params data is overwritten using values from Liu 2014.
+    """
+    ###################################################################
+    # User specified parameters (you may change these if you wish to
+    # run a different scenario from Liu 2014)
+    ###################################################################
+
+    # densities
+    nAr = 1.61e22     # background number density of Ar [1/m^3] (corresponds to p = 500 mTorr)
+    np0 = 8e16        # "nominal" electron density [1/m^3]
+
+    # masses
+    # me = 9.10938356e-31        # mass of an electron [kg]
+    # me = 5.489e-4              # mass of an electron [u]
+    me = 0.511e6                 # mass of an electron [eV/c2]
+    # mAr = 39.948               # mass of an argon atom [u]
+    # mAr = 39.948 * 1.66054e-27 # mass of an argon atom [kg]
+    mAr = 37.2158e9              # mass of an argon atom [eV/c2]
+    # u = 931.4941e6             # eV/c2
+    c = 299792458                # speed of light [m/s]
+    se = 40                      # momentum cross section [A^2]
+
+    # nominal electron energy
+    e0 = 1.0  # [eV]
+
+    # pressure
+    p  = 66.6*1.5      # [J/m^3] *1.5 to convert it to energy (500m Torr)
+
+    # gas energy at the wall
+    Tg0 = 0.038778    # 3/2*300K*kB ~ (p0 - nT[:,0])/ntot
+
+    # characteristics of driving voltage
+    V0  = inputV0                 # amplitude of driving voltage [V]
+    verticalShift = inputVDC      # DC voltage (vertical shift in driving voltage)
+    tau = (1./13.56e6)             # period of driving voltage [s]
+    L   = 2.00*0.005              # half-gap-width [m] (gap width is 2 cm)
+    electrodeArea = np.pi*0.05**2 # electrode area [m^2] (electrode diameter = 0.1 m)
+
+    # transport parameters
+    nmue = 9.66e21   # argon number density times electron mobility [1/(V*cm*s)]
+    nmum = 0.0
+    nmur = 0.0
+    nmu4p = 0.0
+    nmui = 8.0e19
+    nDe  = 3.86e22   # argon number density times electron diffusivity [1/(cm*s)]
+    nDi  = 2.07e18   # argon number density times ion diffusivity [1/(cm*s)]
+    nDm  = 2.42e18   # argon number density times AR(m) diffusivity [1/(cm*s)]
+    nDr  = 2.42e18
+    nD4p = 2.42e18
+
+    # reaction parameters (NB: k_i = Ck*Ee^B*exp(-A/Ee))
+    #                          Ee = 3/2*Te (Te in eV)
+    #                          -> k_i = [Ck*(2/3)^B] * Ee^B * exp[-(3/2)*A/Ee]
+    # nominal
+    Ck = np.array([2.0e-13,2.1e-15,5.0e-16,6.4e-16,2.1e-21,1.32e8,1.72e7,1.50e7,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0e-18,4.0e-19,0.0,0.0,0.0,0.0,2.5e-17,2.5e-17,1.0e-15,1.0e-15,0.0,0.0]) # pre-exponential factors [m^3/s]
+    B  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-0.5,0,0,0,0,0,0,0,0,0,0]) # Temperature Power
+    A  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]) # activation temperature [eV]
+    dH = np.array([0.0,-7.541,-10.577,-7.393,0.0,0.0,0.0,0.0,11.577,11.725,13.168,15.76,-11.577,4.183,0.148,1.592,2.592,-1.444,-1.592,-11.725,-0.148,1.444,0.0,0.0,-4.183,-4.035,-2.592,-15.76,0.0,0.0,-8.985,-9.133,4.035,-13.168]) # energy lost per electron due to ionization rxn [eV]
+    dEps = np.array([0.0,15.76,11.577,11.725,13.168,0.0]) # E, AR+, AR(m), AR(r), AR(4p), AR
+
+    # BC parameters
+    # ks = 1.19e7  # electron recombination rate [cm/s]
+    ks = 1.366109824889323e7 # electron recombination rate [cm/s/eV]
+
+    ###################################################################
+    # Constants of nature (probably shouldn't change unless you have
+    # root privileges on universe)
+    ###################################################################
+    qe   = 1.6e-19   # unit charge [C]
+    eps0 = 8.86e-12  # permittivity of free space [F/m]
+    kB   = 1.38e-23  # Boltzmann constant [J/K]
+    # kB   = 8.62e-5 # Boltzmann constant [eV/K]
+
+
+    ###################################################################
+    # Calculate non-dimensional parameters
+    ###################################################################
+
+    # 1) Convert input units to base SI (except eV)
+    nDe  *= 100. # 1/(m*s)
+    nDi  *= 100. # 1/(m*s)
+    nDm  *= 100.
+    nDr  *= 100.
+    nD4p *= 100.
+    nmue *= 100. # 1/(V*m*s)
+    nmui *= 100. # 1/(V*m*s)
+    ks   *= 0.01 # m/s
+    se   *= 1.0e-20  # m^2
+
+    # 2) Compute "raw" transport parameters
+    De  = nDe/nAr
+    Di  = nDi/nAr
+    Dm  = nDm/nAr
+    Dr  = nDr/nAr
+    D4p = nD4p/nAr
+
+    mue = nmue/nAr
+    mui = nmui/nAr
+    mum = nmum/nAr
+    mur = nmur/nAr
+    mu4p = nmu4p/nAr
+
+    # 3) Compute non-dimensional properties required by solver
+    De    = De*tau/(L*L)
+    Di    = Di*tau/(L*L)
+    Dm    = Dm*tau/(L*L)
+    Dr    = Dr*tau/(L*L)
+    D4p   = D4p*tau/(L*L)
+
+    mue   = mue*V0*tau/(L*L)
+    mui   = mui*V0*tau/(L*L)
+    mum   = mum*V0*tau/(L*L)
+    mur   = mur*V0*tau/(L*L)
+    mu4p  = mu4p*V0*tau/(L*L)
+
+    Ck[0:4]  *= tau*np0
+    Ck[4]    *= tau*nAr
+    Ck[5:8]  *= tau
+    Ck[8:12] *= tau*nAr
+    Ck[12:24]  *= tau*np0
+    Ck[24:28] *= tau*np0*np0
+    Ck[28:30] *= tau*nAr
+    Ck[30:] *= tau*np0
+    A        = A*1.5/e0  # 1.5 to convert from temperature to energy
+    dH       = dH/e0
+    qStar    = V0/e0 # qe*V0/e0, since e0 in eV, need qe*V0 in eV, which is just V0 in V
+    alpha    = qe*np0*L*L/(V0*eps0)
+    ks       = ks*tau/L
+    p0       = p/qe/np0
+    kappaB   = 4.878171165833662*1.6129 # non-dimensional thermal conductivity of background specie
+                                # (2/3)*tau/L**2*Kb/np0/kB,
+                                # where Kb is the thermal conductivity of background specie
+
+    params.beta = np.array([[0,1,1,1,0,0,0,0,1,1,1,2,1,2,1,1,2,1,1,1,1,1,0,0,1,1,1,1,0,0,1,1,2,1],                     # E
+                            [0,1,1,1,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0],                     # AR+
+                            [0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,1,0,0,0,0,0],                     # AR(m)
+                            [0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0],                     # AR(r)
+                            [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0],                     # AR(4p)
+                            [2,1,1,1,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1]], dtype=np.int64)    # AR
+
+    params.alfa = np.array([[0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,0,0,0,0,1,1],                     # E
+                            [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0],                     # AR+
+                            [2,1,0,2,1,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],                     # AR(m)
+                            [0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,1,0],                     # AR(r)
+                            [0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,0,1],                     # AR(4p)
+                            [0,0,0,0,1,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0]], dtype=np.int64)    # AR
+    
+    # Rxn1:  2AR(m)         ->   2AR
+    # Rxn2:  AR(m) + AR(r)  ->   E + AR+ + AR
+    # Rxn3:  2AR(4p)        ->   E + AR+ + AR
+    # Rxn4:  2AR(m)         ->   E + AR+ + AR
+    # Rxn5:  AR(m) + AR     ->   2AR
+    # Rxn6:  AR(r)          ->   AR + hv
+    # Rxn7:  AR(4p)         ->   AR(m) + hv
+    # Rxn8:  AR(4p)         ->   AR(r) + hv
+    # Rxn9:  E + AR         ->   E + AR(m)
+    # Rxn10: E + AR         ->   E + AR(r)
+    # Rxn11: E + AR         ->   E + AR(4p)
+    # Rxn12: E + AR         ->   2E + AR+
+    # Rxn13: E + AR(m)      ->   E + AR
+    # Rxn14: E + AR(m)      ->   2E + AR+
+    # Rxn15: E + AR(m)      ->   E + AR(r)
+    # Rxn16: E + AR(m)      ->   E + AR(4p)
+    # Rxn17: E + AR(4p)     ->   2E + AR+
+    # Rxn18: E + AR(4p)     ->   E + AR(r)
+    # Rxn19: E + AR(4p)     ->   E + AR(m)
+    # Rxn20: E + AR(r)      ->   E + AR
+    # Rxn21: E + AR(r)      ->   E + AR(m)
+    # Rxn22: E + AR(r)      ->   E + AR(4p)
+    # Rxn23: E + AR+        ->   AR(m) + hv
+    # Rxn24: E + AR+        ->   AR(4p) + hv
+    # Rxn25: 2E + AR+       ->   E + AR(m)
+    # Rxn26: 2E + AR+       ->   E + AR(r)
+    # Rxn27: 2E + AR+       ->   E + AR(4p)
+    # Rxn28: 2E + AR+       ->   E + AR
+    # Rxn29: AR + AR(4p)    ->   AR + AR(m)
+    # Rxn30: AR + AR(4p)    ->   AR + AR(r)
+    # Rxn31: AR(m) + AR(4p) ->   E + AR+ + AR
+    # Rxn32: AR(r) + AR(4p) ->   E + AR+ + AR
+    # Rxn33: E + AR(r)      ->   2E + AR+
+    # Rxn34: E + AR(4p)     ->   E + AR
+
+    rxnNameDict = { 0: "2Ar(m) => 2Ar",
+                    1: "Ar(m) + Ar(r) => E + Ar+ + Ar",
+                    2: "2Ar(4p) => E + Ar+ + Ar",
+                    3: "2Ar(m) => E + Ar+ + Ar",
+                    4: "Ar(m) + Ar => 2Ar",
+                    5: "Ar(r) => Ar",
+                    6: "Ar(4p) => Ar(m)",
+                    7: "Ar(4p) => Ar(r)",
+                    8: "Excitation_Metastable",
+                    9: "Excitation_Resonant",
+                   10: "Excitation_4p",
+                   11: "Ionization",
+                   12: "DeExcitation_Metastable",
+                   13: "StepIonization_Metastable",
+                   14: "E + Ar(m) => E + Ar(r)",
+                   15: "E + Ar(m) => E + Ar(4p)",
+                   16: "StepIonization_4p",
+                   17: "E + Ar(4p) => E + Ar(r)",
+                   18: "E + Ar(4p) => E + Ar(m)",
+                   19: "DeExcitation_Resonant",
+                   20: "E + Ar(r) => E + Ar(m)",
+                   21: "E + Ar(r) => E + Ar(4p)",
+                   22: "E + Ar+ => Ar(m)",
+                   23: "E + Ar+ => Ar(4p)",
+                   24: "3BdyRecomb_Metastable",
+                   25: "3BdyRecomb_Resonant",
+                   26: "3BdyRecomb_4p",
+                   27: "3BdyRecomb_Ground",
+                   28: "Ar + Ar(4p) => Ar + Ar(m)",
+                   29: "Ar + Ar(4p) => Ar + Ar(r)",
+                   30: "Ar(m) + Ar(4p) => E + Ar+ + Ar",
+                   31: "Ar(r) + Ar(4p) => E + Ar+ + Ar",
+                   32: "StepIonization_Resonant",
+                   33: "DeExcitation_4p"}
+
+    # 4) Set values in params class
+    params.D[0]    = De
+    params.D[1]    = Di
+    params.D[2]    = Dm
+    params.D[3]    = Dr
+    params.D[4]    = D4p
+
+    params.mu[0]   = mue
+    params.mu[1]   = mui
+    params.mu[2]   = mum
+    params.mu[3]   = mur
+    params.mu[4]   = mu4p
+
+    params.A[:]    = Ck[:]
+    params.B[:]    = B[:]
+    params.C[:]    = A[:]
+
+    # Account for the 2/3 term to convert from electron temperature to electron energy
+    for i in range(len(params.A)):
+        params.A[i] *= (2/3)**(params.B[i])
+
+    params.dH[:]   = dH[:]
+    params.dEps[:] = dEps[:]
+    params.qStar   = qStar
+    params.alpha   = alpha
+    params.ks      = ks
+    params.gam     = gam
+    params.kappaB  = kappaB
+    params.nAronp0 = nAr / np0
+    params.p0      = p0
+    params.Tg0     = Tg0
+    params.EC      = 2.0 * me / mAr \
+        * np.sqrt(16.0 * (me + mAr) * e0 * c**2
+                  / (3.0 * np.pi * me * mAr)) * se * nAr * tau
+    # params.EC = 2.0 * me / mAr * 3.8e9 * tau
+
+    params.verticalShift = verticalShift / V0
+
+    # Parameters needed to compute the current with dimensions
+    params.V0Ltau  = V0 / (L * tau)
+    params.V0L     = V0 / L
+    params.LLV0tau = (L*L) / (V0*tau)
+    params.tauL    = L / tau
+    params.np0     = np0           # "nominal" electron density [1/m^3]
+    params.qe      = qe            # unit charge [C]
+    params.eps0    = eps0          # unit charge [C]
+    params.eArea   = electrodeArea # electrode area [m^2]
+
+
+    reactionExpressionslist = [f"{params.A[0]} * energy**{params.B[0]} * np.exp(-{params.C[0]} / energy)",
+                               f"{params.A[1]} * energy**{params.B[1]} * np.exp(-{params.C[1]} / energy)",
+                               f"{params.A[2]} * energy**{params.B[2]} * np.exp(-{params.C[2]} / energy)",
+                               f"{params.A[3]} * energy**{params.B[3]} * np.exp(-{params.C[3]} / energy)",
+                               f"{params.A[4]} * energy**{params.B[4]} * np.exp(-{params.C[4]} / energy)",
+                               f"{params.A[5]} * energy**{params.B[5]} * np.exp(-{params.C[5]} / energy)",
+                               f"{params.A[6]} * energy**{params.B[6]} * np.exp(-{params.C[6]} / energy)",
+                               f"{params.A[7]} * energy**{params.B[7]} * np.exp(-{params.C[7]} / energy)",
+                               f"{params.A[8]} * energy**{params.B[8]} * np.exp(-{params.C[8]} / energy)",
+                               f"{params.A[9]} * energy**{params.B[9]} * np.exp(-{params.C[9]} / energy)",
+                               f"{params.A[10]} * energy**{params.B[10]} * np.exp(-{params.C[10]} / energy)",
+                               f"{params.A[11]} * energy**{params.B[11]} * np.exp(-{params.C[11]} / energy)",
+                               f"{params.A[12]} * energy**{params.B[12]} * np.exp(-{params.C[12]} / energy)",
+                               f"{params.A[13]} * energy**{params.B[13]} * np.exp(-{params.C[13]} / energy)",
+                               f"{params.A[14]} * energy**{params.B[14]} * np.exp(-{params.C[14]} / energy)",
+                               f"{params.A[15]} * energy**{params.B[15]} * np.exp(-{params.C[15]} / energy)",
+                               f"{params.A[16]} * energy**{params.B[16]} * np.exp(-{params.C[16]} / energy)",
+                               f"{params.A[17]} * energy**{params.B[17]} * np.exp(-{params.C[17]} / energy)",
+                               f"{params.A[18]} * energy**{params.B[18]} * np.exp(-{params.C[18]} / energy)",
+                               f"{params.A[19]} * energy**{params.B[19]} * np.exp(-{params.C[19]} / energy)",
+                               f"{params.A[20]} * energy**{params.B[20]} * np.exp(-{params.C[20]} / energy)",
+                               f"{params.A[21]} * energy**{params.B[21]} * np.exp(-{params.C[21]} / energy)",
+                               f"{params.A[22]} * energy**{params.B[22]} * np.exp(-{params.C[22]} / energy)",
+                               f"{params.A[23]} * energy**{params.B[23]} * np.exp(-{params.C[23]} / energy)",
+                               f"{params.A[24]} * energy**{params.B[24]} * np.exp(-{params.C[24]} / energy)",
+                               f"{params.A[25]} * energy**{params.B[25]} * np.exp(-{params.C[25]} / energy)",
+                               f"{params.A[26]} * energy**{params.B[26]} * np.exp(-{params.C[26]} / energy)",
+                               f"{params.A[27]} * energy**{params.B[27]} * np.exp(-{params.C[27]} / energy)",
+                               f"{params.A[28]} * energy**{params.B[28]} * np.exp(-{params.C[28]} / energy)",
+                               f"{params.A[29]} * energy**{params.B[29]} * np.exp(-{params.C[29]} / energy)",
+                               f"{params.A[30]} * energy**{params.B[30]} * np.exp(-{params.C[30]} / energy)",
+                               f"{params.A[31]} * energy**{params.B[31]} * np.exp(-{params.C[31]} / energy)",
+                               f"{params.A[32]} * energy**{params.B[32]} * np.exp(-{params.C[32]} / energy)",
+                               f"{params.A[33]} * energy**{params.B[33]} * np.exp(-{params.C[33]} / energy)"]
+
+
+    reactionTExpressionslist = [f"{params.A[0]} * (energy**({params.B[0]}-1)) * np.exp(-{params.C[0]}/energy) * ({params.B[0]} + {params.C[0]}/energy)",
+                                f"{params.A[1]} * (energy**({params.B[1]}-1)) * np.exp(-{params.C[1]}/energy) * ({params.B[1]} + {params.C[1]}/energy)",
+                                f"{params.A[2]} * (energy**({params.B[2]}-1)) * np.exp(-{params.C[2]}/energy) * ({params.B[2]} + {params.C[2]}/energy)",
+                                f"{params.A[3]} * (energy**({params.B[3]}-1)) * np.exp(-{params.C[3]}/energy) * ({params.B[3]} + {params.C[3]}/energy)",
+                                f"{params.A[4]} * (energy**({params.B[4]}-1)) * np.exp(-{params.C[4]}/energy) * ({params.B[4]} + {params.C[4]}/energy)",
+                                f"{params.A[5]} * (energy**({params.B[5]}-1)) * np.exp(-{params.C[5]}/energy) * ({params.B[5]} + {params.C[5]}/energy)",
+                                f"{params.A[6]} * (energy**({params.B[6]}-1)) * np.exp(-{params.C[6]}/energy) * ({params.B[6]} + {params.C[6]}/energy)",
+				f"{params.A[7]} * (energy**({params.B[7]}-1)) * np.exp(-{params.C[7]}/energy) * ({params.B[7]} + {params.C[7]}/energy)",
+		                f"{params.A[8]} * (energy**({params.B[8]}-1)) * np.exp(-{params.C[8]}/energy) * ({params.B[8]} + {params.C[8]}/energy)",
+                                f"{params.A[9]} * (energy**({params.B[9]}-1)) * np.exp(-{params.C[9]}/energy) * ({params.B[9]} + {params.C[9]}/energy)",
+                                f"{params.A[10]} * (energy**({params.B[10]}-1)) * np.exp(-{params.C[10]}/energy) * ({params.B[10]} + {params.C[10]}/energy)",
+                                f"{params.A[11]} * (energy**({params.B[11]}-1)) * np.exp(-{params.C[11]}/energy) * ({params.B[11]} + {params.C[11]}/energy)",
+                                f"{params.A[12]} * (energy**({params.B[12]}-1)) * np.exp(-{params.C[12]}/energy) * ({params.B[12]} + {params.C[12]}/energy)",
+                                f"{params.A[13]} * (energy**({params.B[13]}-1)) * np.exp(-{params.C[13]}/energy) * ({params.B[13]} + {params.C[13]}/energy)",
+                                f"{params.A[14]} * (energy**({params.B[14]}-1)) * np.exp(-{params.C[14]}/energy) * ({params.B[14]} + {params.C[14]}/energy)",
+                                f"{params.A[15]} * (energy**({params.B[15]}-1)) * np.exp(-{params.C[15]}/energy) * ({params.B[15]} + {params.C[15]}/energy)",
+                                f"{params.A[16]} * (energy**({params.B[16]}-1)) * np.exp(-{params.C[16]}/energy) * ({params.B[16]} + {params.C[16]}/energy)",
+                                f"{params.A[17]} * (energy**({params.B[17]}-1)) * np.exp(-{params.C[17]}/energy) * ({params.B[17]} + {params.C[17]}/energy)",
+                                f"{params.A[18]} * (energy**({params.B[18]}-1)) * np.exp(-{params.C[18]}/energy) * ({params.B[18]} + {params.C[18]}/energy)",
+                                f"{params.A[19]} * (energy**({params.B[19]}-1)) * np.exp(-{params.C[19]}/energy) * ({params.B[19]} + {params.C[19]}/energy)",
+                                f"{params.A[20]} * (energy**({params.B[20]}-1)) * np.exp(-{params.C[20]}/energy) * ({params.B[20]} + {params.C[20]}/energy)",
+                                f"{params.A[21]} * (energy**({params.B[21]}-1)) * np.exp(-{params.C[21]}/energy) * ({params.B[21]} + {params.C[21]}/energy)",
+                                f"{params.A[22]} * (energy**({params.B[22]}-1)) * np.exp(-{params.C[22]}/energy) * ({params.B[22]} + {params.C[22]}/energy)",
+                                f"{params.A[23]} * (energy**({params.B[23]}-1)) * np.exp(-{params.C[23]}/energy) * ({params.B[23]} + {params.C[23]}/energy)",
+                                f"{params.A[24]} * (energy**({params.B[24]}-1)) * np.exp(-{params.C[24]}/energy) * ({params.B[24]} + {params.C[24]}/energy)",
+                                f"{params.A[25]} * (energy**({params.B[25]}-1)) * np.exp(-{params.C[25]}/energy) * ({params.B[25]} + {params.C[25]}/energy)",
+                                f"{params.A[26]} * (energy**({params.B[26]}-1)) * np.exp(-{params.C[26]}/energy) * ({params.B[26]} + {params.C[26]}/energy)",
+                                f"{params.A[27]} * (energy**({params.B[27]}-1)) * np.exp(-{params.C[27]}/energy) * ({params.B[27]} + {params.C[27]}/energy)",
+                                f"{params.A[28]} * (energy**({params.B[28]}-1)) * np.exp(-{params.C[28]}/energy) * ({params.B[28]} + {params.C[28]}/energy)",
+                                f"{params.A[29]} * (energy**({params.B[29]}-1)) * np.exp(-{params.C[29]}/energy) * ({params.B[29]} + {params.C[29]}/energy)",
+                                f"{params.A[30]} * (energy**({params.B[30]}-1)) * np.exp(-{params.C[30]}/energy) * ({params.B[30]} + {params.C[30]}/energy)",
+                                f"{params.A[31]} * (energy**({params.B[31]}-1)) * np.exp(-{params.C[31]}/energy) * ({params.B[31]} + {params.C[31]}/energy)",
+                                f"{params.A[32]} * (energy**({params.B[32]}-1)) * np.exp(-{params.C[32]}/energy) * ({params.B[32]} + {params.C[32]}/energy)",
+                                f"{params.A[33]} * (energy**({params.B[33]}-1)) * np.exp(-{params.C[33]}/energy) * ({params.B[33]} + {params.C[33]}/energy)"]
+           
+
+    reactionExpressionTypelist =  np.array([False,False,False,False,False,False,False,False, # Rxns 1-8
+                                           True,True,True,True,True,True,True,True,True,True,True,True,True,True, # Rxns 9-21
+                                           False,False,True,True,True,True,False,False,False,False,True,True]) # Rxns 22-34
+
+    thresholded_rxn = np.array([False, False, False, False, False, False, False, False,
+                                True, True, True, True, False, True, True, True, True, False, False, False, False,
+                                True, False, False, False, False, False, False, False, False, False, False, True,
+                                False])
+    reactionsList = []
+    LOGFilename = 'interpolationSample.log'
+    f = open(LOGFilename, 'w')
+
+    for i in range(Nr):
+        if reactionExpressionTypelist[i]:
+            if i < 14 or i == 16 or i == 19 or i > 23:
+                f = h5.File("../../../BOLSIGChemistry_NominalRates/{0:s}.h5".format(rxnNameDict[i]), 'r')
+                dataset = f["table"]
+            else:
+                f = h5.File("../../../BOLSIGChemistry_NominalRates/StepExcitation.h5", 'r')
+                dataset = f[rxnNameDict[i]]
+
+            Te = dataset[:,0]
+            Te /= 11604
+            rateCoeff = dataset[:,1]
+            if i > 23 and i < 28:
+                rateCoeff /= 6.022e23**2
+            else:
+                rateCoeff /= 6.022e23
+
+            ## Removing BOLSIG failures
+            fail_inds = []
+            for j in range(len(rateCoeff)):
+                if rateCoeff[j] == 0.0 and j > np.nonzero(rateCoeff)[0][0]:
+                    fail_inds.append(j)
+
+            Te = np.delete(Te, fail_inds)
+            rateCoeff = np.delete(rateCoeff, fail_inds)
+
+            # Sorting mean energy array and rate coefficient array based on
+            # the mean energy array.
+            Teinds = Te.argsort()
+            rateCoeff = rateCoeff[Teinds]
+            Te = Te[Teinds]
+
+            # Find duplicates
+            TeDuplicateinds = np.where(np.abs(np.diff(Te, axis=0)) > 0.0)
+            TeDuplicateindsForLog = np.where(np.abs(np.diff(Te, axis=0)) == 0.0)
+            rateCoeff = rateCoeff[TeDuplicateinds]
+            Te = Te[TeDuplicateinds]
+
+            # Nondimensionalization of mean energy.
+            Te *= 1.5
+
+            # Find first non-zero value of the coefficient rate.
+            I = np.nonzero(rateCoeff)
+
+            diffRateCoeff = [j-i for i, j in zip(rateCoeff[:-1], rateCoeff[1:])]
+            diffTe = [j-i for i, j in zip(Te[:-1], Te[1:])]
+
+            Monotonicity = np.asarray([j/i for i, j in zip(diffTe, diffRateCoeff)])
+            Monotonicity = np.insert(Monotonicity, 0, 0.0, axis=0)
+
+            Nan = np.isnan(Monotonicity)
+            Inf = np.isinf(Monotonicity)
+            if thresholded_rxn[i] == True:
+                indexPositive = np.where(Monotonicity>0.0)
+            else:
+                indexPositive = np.where(Monotonicity<0.0)
+            Positive = np.full(Monotonicity.shape, False, dtype=bool)
+            Positive[indexPositive] = True
+            
+            indices = Nan + Inf + Positive
+
+
+            #lastFalse = np.where(indices==False)[-1][-1] + 2
+            for k in range(len(Te)):
+               if Te[k] < 4.5 and indices[k] == False:
+                  lastFalse = k + 2
+
+            # Transformation to log scale.
+            TeLog = np.log(Te)
+
+            # Compute the slope of the rate coefficient between its first two non-zero values.
+            # Finite differences are used.
+            dydx = (rateCoeff[lastFalse + 1] - rateCoeff[lastFalse]) \
+                 / (Te[lastFalse + 1] - Te[lastFalse])
+
+            # Arrhenius form: kf = A * exp(-C / Te)
+            C = Te[lastFalse]**2.0*dydx / rateCoeff[lastFalse]
+
+            # Compute pre-exponential coefficient, A, in log scale.
+            ALog = np.log(rateCoeff[lastFalse]) + C / Te[lastFalse]
+
+            # Transform rate coefficient in log scale.
+            rateCoeffLog = np.zeros(rateCoeff.shape)
+            rateCoeffLog[lastFalse:] = np.log(rateCoeff[lastFalse:])
+            # For the troublesome values, we use the Arrhenius form.
+            rateCoeffLog[0:lastFalse] = ALog - C / Te[0:lastFalse]
+            # Nondimensionalization in log scale.
+            if (i < 12):
+                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
+            elif i > 23 and i < 28:
+                rateCoeffLog += - np.log(1.0/tau) + 2*np.log(np0)
+            else:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(np0)
+
+            # Interpolation in log scale.
+            reactionExpressionsLog = CubicSpline(TeLog, rateCoeffLog)
+            # Gradient in log scale
+            reactionTExpressionsLog = CubicSpline.derivative(reactionExpressionsLog)
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf_log = reactionExpressionsLog,
+                                kf_T_log = reactionTExpressionsLog)
+            reactionsList.append(reaction)
+
+            logging.basicConfig(filename=LOGFilename)
+            logging.warning('Interpolation info (Reaction %s):', i + 1)
+            logging.warning('First non-zero entry in the rate coefficient: %s', I[0][0])
+            logging.warning('Monotonicity of the rate coefficient start from entry: %s', lastFalse)
+            logging.warning('Position of possible duplicates in mean energy array: %s',
+                          TeDuplicateindsForLog[0])
+
+        else:
+            rxn   = eval("lambda energy :" + reactionExpressionslist[i])
+            rxn_T = eval("lambda energy :" + reactionTExpressionslist[i])
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf = rxn, kf_T = rxn_T)
+            reactionsList.append(reaction)
+
+    params.reactionsList = reactionsList
+
+    ## Electron Transport Data
+    diffList = []
+    transport = h5.File("../../../BOLSIGChemistry_NominalRates/nominal_transport.h5", 'r')
+    NDe_v_Te = transport["diffusivity"]
+    Te_trans = NDe_v_Te[:,0]
+    Te_trans /= 11604
+    De_interp = (NDe_v_Te[:,1]/nAr)*tau/(L*L)
+    De_spline = CubicSpline(Te_trans, De_interp)
+    De_Te_spline = CubicSpline.derivative(De_spline)
+    diffusivity = Diffusivity(interpolate = True, D_expression = De_spline, D_T_expression = De_Te_spline)
+    diffList.append(diffusivity)
+
+    Ns = 6
+    for i in range(1, Ns):
+        diffList.append(Diffusivity(interpolate = False))
+
+    params.diffusivityList = diffList
+
+    muList = []
+    Nmue_v_Te = transport["mobility"]
+    mue_interp = (Nmue_v_Te[:,1]/nAr)*V0*tau/(L*L)
+    mue_spline = CubicSpline(Te_trans, mue_interp)
+    mue_Te_spline = CubicSpline.derivative(mue_spline)
+    mobility = Mobility(interpolate = True, mu_expression = mue_spline, mu_T_expression = mue_Te_spline)
+    muList.append(mobility)
+
+    Ns = 6
+    for i in range(1, Ns):
+        muList.append(Mobility(interpolate = False))
+
+    params.mobilityList = muList
+    
+    # 5) Dump to screen
+    params.print()
diff --git a/psaapProperties_6Species_5Torr.py b/psaapProperties_6Species_5Torr.py
new file mode 100755
index 000000000..ea5a015d5
--- /dev/null
+++ b/psaapProperties_6Species_5Torr.py
@@ -0,0 +1,533 @@
+import numpy as np
+from scipy.interpolate import CubicSpline
+import csv
+#import matplotlib.pyplot as plt
+#import matplotlib.colors as mcolors
+import h5py as h5
+
+import logging
+
+class Reaction(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Diffusivity(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Mobility(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+
+def setPsaapProperties_6Species_5Torr(gam, inputV0, inputVDC, params, Nr, iSample):
+    """Sets non-dimensional properties corresponding to Liu 2014 paper.
+
+    Inputs:
+      gam : Secondary electron emission coefficient
+      params : chebSolver.modelParams class
+
+    Outputs: None
+      params data is overwritten using values from Liu 2014.
+    """
+    ###################################################################
+    # User specified parameters (you may change these if you wish to
+    # run a different scenario from Liu 2014)
+    ###################################################################
+
+    # densities
+    nAr = 1.61e23     # background number density of Ar [1/m^3] (corresponds to p=100 mTorr)
+    np0 = 8e16        # "nominal" electron density [1/m^3]
+
+    # masses
+    # me = 9.10938356e-31        # mass of an electron [kg]
+    # me = 5.489e-4              # mass of an electron [u]
+    me = 0.511e6                 # mass of an electron [eV/c2]
+    # mAr = 39.948               # mass of an argon atom [u]
+    # mAr = 39.948 * 1.66054e-27 # mass of an argon atom [kg]
+    mAr = 37.2158e9              # mass of an argon atom [eV/c2]
+    # u = 931.4941e6             # eV/c2
+    c = 299792458                # speed of light [m/s]
+    se = 40                      # momentum cross section [A^2]
+
+    # nominal electron energy
+    e0 = 1.0  # [eV]
+
+    # pressure
+    p  = 666.65*1.5      # [J/m^3] *1.5 to convert it to energy (1 Torr)
+
+    # gas energy at the wall
+    Tg0 = 0.038778    # 3/2*300K*kB ~ (p0 - nT[:,0])/ntot
+
+    # characteristics of driving voltage
+    V0  = inputV0                 # amplitude of driving voltage [V]
+    verticalShift = inputVDC      # DC voltage (vertical shift in driving voltage)
+    tau = (1./13.56e6)             # period of driving voltage [s]
+    L   = 2.00*0.005              # half-gap-width [m] (gap width is 2 cm)
+    electrodeArea = np.pi*0.05**2 # electrode area [m^2] (electrode diameter = 0.1 m)
+
+    # transport parameters
+    nmue = 9.66e21   # argon number density times electron mobility [1/(V*cm*s)]
+    nmum = 0.0
+    nmur = 0.0
+    nmu4p = 0.0
+    nmui = 8.0e19
+    nDe  = 3.86e22   # argon number density times electron diffusivity [1/(cm*s)]
+    nDi  = 2.07e18   # argon number density times ion diffusivity [1/(cm*s)]
+    nDm  = 2.42e18   # argon number density times AR(m) diffusivity [1/(cm*s)]
+    nDr  = 2.42e18
+    nD4p = 2.42e18
+
+    # reaction parameters (NB: k_i = Ck*Ee^B*exp(-A/Ee))
+    #                          Ee = 3/2*Te (Te in eV)
+    #                          -> k_i = [Ck*(2/3)^B] * Ee^B * exp[-(3/2)*A/Ee]
+    # nominal
+    Ck = np.array([2.0e-7,2.1e-9,5.0e-10,6.4e-10,2.1e-15,1.32e8,0.0,3.0e7,3.0e7,0.0,0.0,0.0,0.0,4.3e-10,0.0,3.7e-8,8.9e-7,1.8e-7,3.0e-7,3.0e-7,4.3e-10,9.1e-7,8.9e-7]) # pre-exponential factors [cm^3/s]
+    B  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0.74,0,0,0.51,0.61,0.51,0.51,0.74,0,0.51])
+    A  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.59,2.61,0,0,0,0,1.59]) # activation temperature [eV]
+    dH = np.array([0.0,-7.412,-10.054,-7.336,0.0,0.0,0.0,0.0,0.0,11.548,11.624,12.907,15.76,-11.548,4.212,0.076,1.359,2.853,-1.283,-1.359,-11.624,-0.076,0.983]) # energy lost per electron due to ionization rxn [eV]
+    dEps = np.array([0.0,15.76,11.548,11.624,12.907,0.0]) # E, AR+, AR(m), AR(r), AR(4p), AR
+
+    # BC parameters
+    # ks = 1.19e7  # electron recombination rate [cm/s]
+    ks = 1.366109824889323e7 # electron recombination rate [cm/s/eV]
+
+    ###################################################################
+    # Constants of nature (probably shouldn't change unless you have
+    # root privileges on universe)
+    ###################################################################
+    qe   = 1.6e-19   # unit charge [C]
+    eps0 = 8.86e-12  # permittivity of free space [F/m]
+    kB   = 1.38e-23  # Boltzmann constant [J/K]
+    # kB   = 8.62e-5 # Boltzmann constant [eV/K]
+
+
+    ###################################################################
+    # Calculate non-dimensional parameters
+    ###################################################################
+
+    # 1) Convert input units to base SI (except eV)
+    nDe  *= 100. # 1/(m*s)
+    nDi  *= 100. # 1/(m*s)
+    nDm  *= 100.
+    nDr  *= 100.
+    nD4p *= 100.
+    nmue *= 100. # 1/(V*m*s)
+    nmui *= 100. # 1/(V*m*s)
+    Ck[0:5] *= 1e-6 # m^3/s
+    Ck[5:9] *= 1 # 1/s
+    Ck[9:]  *= 1e-6 # m^3/s
+    ks   *= 0.01 # m/s
+    se   *= 1.0e-20  # m^2
+
+    # 2) Compute "raw" transport parameters
+    De  = nDe/nAr
+    Di  = nDi/nAr
+    Dm  = nDm/nAr
+    Dr  = nDr/nAr
+    D4p = nD4p/nAr
+
+    mue = nmue/nAr
+    mui = nmui/nAr
+    mum = nmum/nAr
+    mur = nmur/nAr
+    mu4p = nmu4p/nAr
+
+    # 3) Compute non-dimensional properties required by solver
+    De    = De*tau/(L*L)
+    Di    = Di*tau/(L*L)
+    Dm    = Dm*tau/(L*L)
+    Dr    = Dr*tau/(L*L)
+    D4p   = D4p*tau/(L*L)
+
+    mue   = mue*V0*tau/(L*L)
+    mui   = mui*V0*tau/(L*L)
+    mum   = mum*V0*tau/(L*L)
+    mur   = mur*V0*tau/(L*L)
+    mu4p  = mu4p*V0*tau/(L*L)
+
+    Ck[0:4]  *= tau*np0
+    Ck[4]    *= tau*nAr
+    Ck[5:9]  *= tau
+    Ck[9:13] *= tau*nAr
+    Ck[13:]  *= tau*np0
+    A        = A*1.5/e0  # 1.5 to convert from temperature to energy
+    dH       = dH/e0
+    qStar    = V0/e0 # qe*V0/e0, since e0 in eV, need qe*V0 in eV, which is just V0 in V
+    alpha    = qe*np0*L*L/(V0*eps0)
+    ks       = ks*tau/L
+    p0       = p/qe/np0
+    kappaB   = 4.878171165833662*1.6129 # non-dimensional thermal conductivity of background specie
+                                # (2/3)*tau/L**2*Kb/np0/kB,
+                                # where Kb is the thermal conductivity of background specie
+
+    params.beta = np.array([[0,1,1,1,0,0,0,0,0,1,1,1,2,1,2,1,1,2,1,1,1,1,1],                     # E
+                            [0,1,1,1,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0],                     # AR+
+                            [0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0],                     # AR(m)
+                            [0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0],                     # AR(r)
+                            [0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1],                     # AR(4p)
+                            [2,1,1,1,2,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0]], dtype=np.int64)    # AR
+
+    params.alfa = np.array([[0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1],                     # E
+                            [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],                     # AR+
+                            [2,1,0,2,1,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0],                     # AR(m)
+                            [0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1],                     # AR(r)
+                            [0,0,2,0,0,0,1,1,1,0,0,0,0,0,0,0,0,1,1,1,0,0,0],                     # AR(4p)
+                            [0,0,0,0,1,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0]], dtype=np.int64)    # AR
+	# Rxn1:  2AR(m)         ->   2AR
+	# Rxn2:  AR(m) + AR(r)  ->   E + AR+ + AR
+	# Rxn3:  2AR(4p)        ->   E + AR+ + AR
+	# Rxn4:  2AR(m)         ->   E + AR+ + AR
+	# Rxn5:  AR(m) + AR     ->   2AR
+	# Rxn6:  AR(r)          ->   AR
+	# Rxn7:  AR(4p)         ->   AR
+	# Rxn8:  AR(4p)         ->   AR(m)
+	# Rxn9:  AR(4p)         ->   AR(r)
+    # Rxn10: E + AR         ->   E + AR(m)
+    # Rxn11: E + AR         ->   E + AR(r)
+    # Rxn12: E + AR         ->   E + AR(4p)
+    # Rxn13: E + AR         ->   2E + AR+
+    # Rxn14: E + AR(m)      ->   E + AR
+    # Rxn15: E + AR(m)      ->   2E + AR+
+    # Rxn16: E + AR(m)      ->   E + AR(r)
+    # Rxn17: E + AR(m)      ->   E + AR(4p)
+    # Rxn18: E + AR(4p)     ->   2E + AR+
+    # Rxn19: E + AR(4p)     ->   E + AR(r)
+    # Rxn20: E + AR(4p)     ->   E + AR(m)
+    # Rxn21: E + AR(r)      ->   E + AR
+    # Rxn22: E + AR(r)      ->   E + AR(m)
+    # Rxn23: E + AR(r)      ->   E + AR(4p)
+
+    rxnNameDict = { 0: "2Ar(m) => 2Ar",
+                    1: "Ar(m) + Ar(r) => E + Ar+ + Ar",
+                    2: "2Ar(4p) => E + Ar+ + Ar",
+                    3: "2Ar(m) => E + Ar+ + Ar",
+                    4: "Ar(m) + Ar => 2Ar",
+                    5: "Ar(r) => Ar",
+                    6: "Ar(4p) => Ar",
+                    7: "Ar(4p) => Ar(m)",
+                    8: "Ar(4p) => Ar(r)",
+                    9: "lumped.metastable",
+                   10: "lumped.resonance",
+                   11: "lumped.2p",
+                   12: "ionization",
+                   13: "E + Ar(m) => E + Ar",
+                   14: "step_ionization",
+                   15: "E + Ar(m) => E + Ar(r)",
+                   16: "E + Ar(m) => E + Ar(4p)",
+                   17: "E + Ar(4p) => 2E + Ar+",
+                   18: "E + Ar(4p) => E + Ar(r)",
+                   19: "E + Ar(4p) => E + Ar(m)",
+                   20: "E + Ar(r) => E + Ar",
+                   21: "E + Ar(r) => E + Ar(m)",
+                   22: "E + Ar(r) => E + Ar(4p)"}
+
+    # 4) Set values in params class
+    params.D[0]    = De
+    params.D[1]    = Di
+    params.D[2]    = Dm
+    params.D[3]    = Dr
+    params.D[4]    = D4p
+
+    params.mu[0]   = mue
+    params.mu[1]   = mui
+    params.mu[2]   = mum
+    params.mu[3]   = mur
+    params.mu[4]   = mu4p
+
+    params.A[:]    = Ck[:]
+    params.B[:]    = B[:]
+    params.C[:]    = A[:]
+
+    # Account for the 2/3 term to convert from electron temperature to electron energy
+    for i in range(len(params.A)):
+        params.A[i] *= (2/3)**(params.B[i])
+
+    params.dH[:]   = dH[:]
+    params.dEps[:] = dEps[:]
+    params.qStar   = qStar
+    params.alpha   = alpha
+    params.ks      = ks
+    params.gam     = gam
+    params.kappaB  = kappaB
+    params.nAronp0 = nAr / np0
+    params.p0      = p0
+    params.Tg0     = Tg0
+    params.EC      = 2.0 * me / mAr \
+        * np.sqrt(16.0 * (me + mAr) * e0 * c**2
+                  / (3.0 * np.pi * me * mAr)) * se * nAr * tau
+    # params.EC = 2.0 * me / mAr * 3.8e9 * tau
+
+    params.verticalShift = verticalShift / V0
+
+    # Parameters needed to compute the current with dimensions
+    params.V0Ltau  = V0 / (L * tau)
+    params.V0L     = V0 / L
+    params.LLV0tau = (L*L) / (V0*tau)
+    params.tauL    = L / tau
+    params.np0     = np0           # "nominal" electron density [1/m^3]
+    params.qe      = qe            # unit charge [C]
+    params.eps0    = eps0          # unit charge [C]
+    params.eArea   = electrodeArea # electrode area [m^2]
+
+
+    reactionExpressionslist = [f"{params.A[0]} * energy**{params.B[0]} * np.exp(-{params.C[0]} / energy)",
+                               f"{params.A[1]} * energy**{params.B[1]} * np.exp(-{params.C[1]} / energy)",
+                               f"{params.A[2]} * energy**{params.B[2]} * np.exp(-{params.C[2]} / energy)",
+                               f"{params.A[3]} * energy**{params.B[3]} * np.exp(-{params.C[3]} / energy)",
+                               f"{params.A[4]} * energy**{params.B[4]} * np.exp(-{params.C[4]} / energy)",
+                               f"{params.A[5]} * energy**{params.B[5]} * np.exp(-{params.C[5]} / energy)",
+                               f"{params.A[6]} * energy**{params.B[6]} * np.exp(-{params.C[6]} / energy)",
+                               f"{params.A[7]} * energy**{params.B[7]} * np.exp(-{params.C[7]} / energy)",
+                               f"{params.A[8]} * energy**{params.B[8]} * np.exp(-{params.C[8]} / energy)",
+                               f"{params.A[9]} * energy**{params.B[9]} * np.exp(-{params.C[9]} / energy)",
+                               f"{params.A[10]} * energy**{params.B[10]} * np.exp(-{params.C[10]} / energy)",
+                               f"{params.A[11]} * energy**{params.B[11]} * np.exp(-{params.C[11]} / energy)",
+                               f"{params.A[12]} * energy**{params.B[12]} * np.exp(-{params.C[12]} / energy)",
+                               f"{params.A[13]} * energy**{params.B[13]} * np.exp(-{params.C[13]} / energy)",
+                               f"{params.A[14]} * energy**{params.B[14]} * np.exp(-{params.C[14]} / energy)",
+                               f"{params.A[15]} * energy**{params.B[15]} * np.exp(-{params.C[15]} / energy)",
+                               f"{params.A[16]} * energy**{params.B[16]} * np.exp(-{params.C[16]} / energy)",
+                               f"{params.A[17]} * energy**{params.B[17]} * np.exp(-{params.C[17]} / energy)",
+                               f"{params.A[18]} * energy**{params.B[18]} * np.exp(-{params.C[18]} / energy)",
+                               f"{params.A[19]} * energy**{params.B[19]} * np.exp(-{params.C[19]} / energy)",
+                               f"{params.A[20]} * energy**{params.B[20]} * np.exp(-{params.C[20]} / energy)",
+                               f"{params.A[21]} * energy**{params.B[21]} * np.exp(-{params.C[21]} / energy)",
+                               f"{params.A[22]} * energy**{params.B[22]} * np.exp(-{params.C[22]} / energy)"]
+
+    reactionTExpressionslist = [f"{params.A[0]} * (energy**({params.B[0]}-1)) * np.exp(-{params.C[0]}/energy) * ({params.B[0]} + {params.C[0]}/energy)",
+                                f"{params.A[1]} * (energy**({params.B[1]}-1)) * np.exp(-{params.C[1]}/energy) * ({params.B[1]} + {params.C[1]}/energy)",
+                                f"{params.A[2]} * (energy**({params.B[2]}-1)) * np.exp(-{params.C[2]}/energy) * ({params.B[2]} + {params.C[2]}/energy)",
+                                f"{params.A[3]} * (energy**({params.B[3]}-1)) * np.exp(-{params.C[3]}/energy) * ({params.B[3]} + {params.C[3]}/energy)",
+                                f"{params.A[4]} * (energy**({params.B[4]}-1)) * np.exp(-{params.C[4]}/energy) * ({params.B[4]} + {params.C[4]}/energy)",
+                                f"{params.A[5]} * (energy**({params.B[5]}-1)) * np.exp(-{params.C[5]}/energy) * ({params.B[5]} + {params.C[5]}/energy)",
+                                f"{params.A[6]} * (energy**({params.B[6]}-1)) * np.exp(-{params.C[6]}/energy) * ({params.B[6]} + {params.C[6]}/energy)",
+				f"{params.A[7]} * (energy**({params.B[7]}-1)) * np.exp(-{params.C[7]}/energy) * ({params.B[7]} + {params.C[7]}/energy)",
+		                f"{params.A[8]} * (energy**({params.B[8]}-1)) * np.exp(-{params.C[8]}/energy) * ({params.B[8]} + {params.C[8]}/energy)",
+                                f"{params.A[9]} * (energy**({params.B[9]}-1)) * np.exp(-{params.C[9]}/energy) * ({params.B[9]} + {params.C[9]}/energy)",
+                                f"{params.A[10]} * (energy**({params.B[10]}-1)) * np.exp(-{params.C[10]}/energy) * ({params.B[10]} + {params.C[10]}/energy)",
+                                f"{params.A[11]} * (energy**({params.B[11]}-1)) * np.exp(-{params.C[11]}/energy) * ({params.B[11]} + {params.C[11]}/energy)",
+                                f"{params.A[12]} * (energy**({params.B[12]}-1)) * np.exp(-{params.C[12]}/energy) * ({params.B[12]} + {params.C[12]}/energy)",
+                                f"{params.A[13]} * (energy**({params.B[13]}-1)) * np.exp(-{params.C[13]}/energy) * ({params.B[13]} + {params.C[13]}/energy)",
+                                f"{params.A[14]} * (energy**({params.B[14]}-1)) * np.exp(-{params.C[14]}/energy) * ({params.B[14]} + {params.C[14]}/energy)",
+                                f"{params.A[15]} * (energy**({params.B[15]}-1)) * np.exp(-{params.C[15]}/energy) * ({params.B[15]} + {params.C[15]}/energy)",
+                                f"{params.A[16]} * (energy**({params.B[16]}-1)) * np.exp(-{params.C[16]}/energy) * ({params.B[16]} + {params.C[16]}/energy)",
+                                f"{params.A[17]} * (energy**({params.B[17]}-1)) * np.exp(-{params.C[17]}/energy) * ({params.B[17]} + {params.C[17]}/energy)",
+                                f"{params.A[18]} * (energy**({params.B[18]}-1)) * np.exp(-{params.C[18]}/energy) * ({params.B[18]} + {params.C[18]}/energy)",
+                                f"{params.A[19]} * (energy**({params.B[19]}-1)) * np.exp(-{params.C[19]}/energy) * ({params.B[19]} + {params.C[19]}/energy)",
+                                f"{params.A[20]} * (energy**({params.B[20]}-1)) * np.exp(-{params.C[20]}/energy) * ({params.B[20]} + {params.C[20]}/energy)",
+                                f"{params.A[21]} * (energy**({params.B[21]}-1)) * np.exp(-{params.C[21]}/energy) * ({params.B[21]} + {params.C[21]}/energy)",
+                                f"{params.A[22]} * (energy**({params.B[22]}-1)) * np.exp(-{params.C[22]}/energy) * ({params.B[22]} + {params.C[22]}/energy)"]
+           
+
+    reactionExpressionTypelist =  np.array([False,False,False,False,False,False,False,False,False,
+                                           True,True,True,True,False,True,True,True,False,True,True,False,True,True])
+
+    reactionsList = []
+    LOGFilename = 'interpolationSample.log'
+    f = open(LOGFilename, 'w')
+
+    for i in range(Nr):
+        if reactionExpressionTypelist[i]:
+            if (i < 15):
+                f = h5.File("../BOLSIGChemistry_6SpeciesRates/{0:s}.h5".format(rxnNameDict[i]), 'r')
+                dataset = f["table"]
+            else:
+                f = h5.File("../BOLSIGChemistry_6SpeciesRates/StepwiseExcitations.nominal.h5", 'r')
+                dataset = f[rxnNameDict[i]]
+
+            Te = dataset[:,0]
+            Te /= 11604
+            rateCoeff = dataset[:,1]
+            rateCoeff /= 6.022e23
+
+            # Sorting mean energy array and rate coefficient array based on
+            # the mean energy array.
+            Teinds = Te.argsort()
+            rateCoeff = rateCoeff[Teinds]
+            Te = Te[Teinds]
+
+            # Find duplicates
+            TeDuplicateinds = np.where(np.abs(np.diff(Te, axis=0)) > 0.0)
+            TeDuplicateindsForLog = np.where(np.abs(np.diff(Te, axis=0)) == 0.0)
+            rateCoeff = rateCoeff[TeDuplicateinds]
+            Te = Te[TeDuplicateinds]
+
+            # Nondimensionalization of mean energy.
+            Te *= 1.5
+
+            # Find first non-zero value of the coefficient rate.
+            I = np.nonzero(rateCoeff)
+
+            diffRateCoeff = [j-i for i, j in zip(rateCoeff[:-1], rateCoeff[1:])]
+            diffTe = [j-i for i, j in zip(Te[:-1], Te[1:])]
+
+            Monotonicity = np.asarray([j/i for i, j in zip(diffTe, diffRateCoeff)])
+            Monotonicity = np.insert(Monotonicity, 0, 0.0, axis=0)
+
+            Nan = np.isnan(Monotonicity)
+            Inf = np.isinf(Monotonicity)
+            indexPositive = np.where(Monotonicity>0.0)
+            Positive = np.full(Monotonicity.shape, False, dtype=bool)
+            Positive[indexPositive] = True
+            
+            indices = Nan + Inf + Positive
+
+
+            #lastFalse = np.where(indices==False)[-1][-1] + 2
+            for k in range(len(Te)):
+               if Te[k] < 4.5 and indices[k] == False:
+                  lastFalse = k + 2
+
+            # Transformation to log scale.
+            TeLog = np.log(Te)
+
+            # Compute the slope of the rate coefficient between its first two non-zero values.
+            # Finite differences are used.
+            dydx = (rateCoeff[lastFalse + 1] - rateCoeff[lastFalse]) \
+                 / (Te[lastFalse + 1] - Te[lastFalse])
+
+            # Arrhenius form: kf = A * exp(-C / Te)
+            C = Te[lastFalse]**2.0*dydx / rateCoeff[lastFalse]
+
+            # Compute pre-exponential coefficient, A, in log scale.
+            ALog = np.log(rateCoeff[lastFalse]) + C / Te[lastFalse]
+
+            # Transform rate coefficient in log scale.
+            rateCoeffLog = np.zeros(rateCoeff.shape)
+            rateCoeffLog[lastFalse:] = np.log(rateCoeff[lastFalse:])
+            # For the troublesome values, we use the Arrhenius form.
+            rateCoeffLog[0:lastFalse] = ALog - C / Te[0:lastFalse]
+            # Nondimensionalization in log scale.
+            if (i < 13):
+                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
+            else:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(np0)
+
+            # Interpolation in log scale.
+            reactionExpressionsLog = CubicSpline(TeLog, rateCoeffLog)
+            # Gradient in log scale
+            reactionTExpressionsLog = CubicSpline.derivative(reactionExpressionsLog)
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf_log = reactionExpressionsLog,
+                                kf_T_log = reactionTExpressionsLog)
+            reactionsList.append(reaction)
+
+            logging.basicConfig(filename=LOGFilename)
+            logging.warning('Interpolation info (Reaction %s):', i + 1)
+            logging.warning('First non-zero entry in the rate coefficient: %s', I[0][0])
+            logging.warning('Monotonicity of the rate coefficient start from entry: %s', lastFalse)
+            logging.warning('Position of possible duplicates in mean energy array: %s',
+                          TeDuplicateindsForLog[0])
+
+        else:
+            rxn   = eval("lambda energy :" + reactionExpressionslist[i])
+            rxn_T = eval("lambda energy :" + reactionTExpressionslist[i])
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf = rxn, kf_T = rxn_T)
+            reactionsList.append(reaction)
+
+    params.reactionsList = reactionsList
+
+    diffList = []
+    # Data from BOLSIG
+    # Te in [eV]
+    # De * N in [1/(m*s)]
+    #NDe_v_Te = np.array([ [-0.05, 1.8e25],         [0.0, 1.8e25],           [0.05, 1.8e25],          [0.1, 1.8e25],
+    #                      [0.2103718, 1.8e25],     [0.2244455, 1.8e25],     [0.2390528, 1.8e25],     [0.2544605, 1.8e25],
+    #                      [0.2710021, 1.8e25],     [0.2886776, 1.8e25],     [0.3078205, 1.8e25],     [0.3286309, 1.8e25],
+    #                      [0.3513089, 1.8e25],     [0.3760546, 1.8e25],     [0.4032682, 1.8e25],     [0.4331498, 1.8e25],
+    #                      [0.4659662, 1.8e25],     [0.5021843, 1.8e25],     [0.5424044, 1.8e25],     [0.5868266, 1.8e25],
+    #                      [0.6359845, 1.8e25],     [0.690345, 1.8e25],      [0.749041, 1.8e25],      [0.813073, 1.8e25],
+    #                      [1.217942, 1.46E+25],    [1.319326, 1.39E+25],    [1.430048, 1.32E+25],    [1.550108, 1.26E+25],
+    #                      [1.681507, 1.20E+25],    [1.823578, 1.15E+25],    [1.977655, 1.09E+25],    [2.143071, 1.05E+25],
+    #                      [2.319826, 9.98E+24],    [2.508587, 9.54E+24],    [2.709354, 9.13E+24],    [2.916124, 8.75E+24],
+    #                      [3.112889, 8.45E+24],    [3.272969, 8.24E+24],    [3.383691, 8.14E+24],    [3.456394, 8.09E+24],
+    #                      [3.505085, 8.07E+24],    [3.544438, 8.05E+24],    [3.579789, 8.03E+24],    [3.616474, 8.01E+24],
+    #                      [3.656494, 7.97E+24],    [3.701183, 7.93E+24],    [3.751875, 7.87E+24],    [3.807903, 7.81E+24],
+    #                      [3.871268, 7.75E+24],    [3.941303, 7.68E+24],    [4.018675, 7.61E+24],    [4.102717, 7.53E+24],
+    #                      [4.193429, 7.5E+24],     [4.290811, 7.5E+24],     [4.39553, 7.5E+24],      [4.507586, 7.5E+24],
+    #                      [5., 7.5E+24],           [6., 7.5E+24],           [7., 7.5E+24],           [8., 7.5E+24],
+    #                      [9., 7.5E+24],           [10., 7.5E+24],          [11., 7.5E+24],          [12., 7.5E+24]])
+
+    transport = h5.File("../BOLSIGChemistry_Transport/nominal_transport.h5", 'r')
+    NDe_v_Te = transport["diffusivity"]
+    Te_trans = NDe_v_Te[:,0]
+    Te_trans /= 11604
+    print("Te_min = {0:.6e}".format(NDe_v_Te[0,0]))
+    print("Te_max = {0:.6e}".format(NDe_v_Te[-1,0]))
+    De_interp = (NDe_v_Te[:,1]/nAr)*tau/(L*L)
+    De_spline = CubicSpline(Te_trans, De_interp)
+    De_Te_spline = CubicSpline.derivative(De_spline)
+    diffusivity = Diffusivity(interpolate = True, D_expression = De_spline, D_T_expression = De_Te_spline)
+    diffList.append(diffusivity)
+
+    Ns = 6
+    for i in range(1, Ns):
+        diffList.append(Diffusivity(interpolate = False))
+
+    params.diffusivityList = diffList
+
+    muList = []
+    # Data from BOLSIG
+    # Te in [eV]
+    # Mue * N in [1/(V*m*s)]
+    #Nmue_v_Te = np.array([[0.02846756, 1.289e+26], [0.02975487, 1.33e+26],   [0.03173586, 1.383e+26],  [0.03477738,1.448e+26],
+    #                      [0.03937968, 1.523e+26], [0.04614973, 1.604e+26],  [0.05561446, 1.679e+26],  [0.0681674,1.735e+26],
+    #                      [0.0835084, 1.749e+26],  [0.1008504, 1.718e+26],   [0.1187927, 1.639e+26],   [0.1363348,1.522e+26],
+    #                      [0.1528764, 1.386e+26],  [0.1682841, 1.245e+26],   [0.1826913, 1.108e+26],   [0.1965649,9.809e+25],
+    #                      [0.2103718, 8.662e+25],  [0.2244455, 7.641e+25],   [0.2390528, 6.74e+25],    [0.2544605,5.947e+25],
+    #                      [0.2710021, 5.249e+25],  [0.2886776, 4.636e+25],   [0.3078205, 4.097e+25],   [0.3286309,3.623e+25],
+    #                      [0.3513089, 3.206e+25],  [0.3760546, 2.839e+25],   [0.4032682, 2.517e+25],   [0.4331498,2.232e+25],
+    #                      [0.4659662, 1.981e+25],  [0.5021843, 1.76e+25],    [0.5424044, 1.565e+25],   [0.5868266,1.392e+25],
+    #                      [0.6359845, 1.239e+25],  [0.690345, 1.103e+25],    [0.749041,  9.8e+24],     [0.813073,8.699e+24],
+    #                      [0.882441, 7.711e+24],   [0.957145, 6.828e+24],    [1.037185,  6.04e+24],    [1.123895,5.344e+24],
+    #                      [1.217942, 4.729e+24],   [1.319326, 4.186e+24],    [1.430048,  3.707e+24],   [1.550108,3.283e+24],
+    #                      [1.681507, 2.907e+24],   [1.823578, 2.574e+24],    [1.977655,  2.278e+24],   [2.143071,2.015e+24],
+    #                      [2.319826, 1.779e+24],   [2.508587, 1.569e+24],    [2.709354,  1.384e+24],   [2.916124,1.228e+24],
+    #                      [3.112889, 1.115e+24],   [3.272969, 1.055e+24],    [3.383691,  1.038e+24],   [3.456394,1.044e+24],
+    #                      [3.505085, 1.054e+24],   [3.544438, 1.062e+24],    [3.579789,  1.064e+24],   [3.616474, 1.059e+24],
+    #                      [3.656494, 1.048e+24],   [3.701183, 1.033e+24],    [3.751875,  1.014e+24],   [3.807903, 9.928e+23],
+    #                      [3.871268, 9.697e+23],   [3.941303,  9.459e+23],   [4.018675, 9.22e+23],     [4.102717, 8.991e+23],
+    #                      [4.193429, 8.773e+23],   [4.290811,  8.57e+23],    [4.39553, 8.383e+23],     [4.507586, 8.21e+23],
+    #                      [4.627646, 8.051e+23],   [4.757711,  7.901e+23],   [4.899115, 7.757e+23],    [5.054526, 7.617e+23],
+    #                      [5.227946, 7.479e+23],   [5.423377,  7.339e+23],   [5.646822, 7.198e+23],    [5.905618, 7.056e+23],
+    #                      [6.20977, 6.91e+23],     [6.571284,  6.757e+23],   [7.01017, 6.607e+23],     [7.54377, 6.458e+23],
+    #                      [8.19743, 6.303e+23],    [9.01117,   6.155e+23],   [10.02501, 6e+23],        [11.30565, 5.843e+23],
+    #                      [12.89978, 5.677e+23],   [14.91412,  5.51e+23],    [17.41537, 5.326e+23],    [20.57028, 5.149e+23],
+    #                      [24.51225, 4.968e+23],   [29.45472,  4.784e+23],   [35.6845, 4.602e+23],     [43.58845, 4.421e+23],
+    #                      [53.86692, 4.269e+23],   [67.367,    4.134e+23],   [85.4427, 4.026e+23],     [100.0, 4.026e+23]])
+
+    Nmue_v_Te = transport["mobility"]
+    #Te = Nmue_v_Te[:,0]
+    #Te /= 11604
+    mue_interp = (Nmue_v_Te[:,1]/nAr)*V0*tau/(L*L)
+    mue_spline = CubicSpline(Te_trans, mue_interp)
+    mue_Te_spline = CubicSpline.derivative(mue_spline)
+    mobility = Mobility(interpolate = True, mu_expression = mue_spline, mu_T_expression = mue_Te_spline)
+    muList.append(mobility)
+
+    Ns = 6
+    for i in range(1, Ns):
+        muList.append(Mobility(interpolate = False))
+
+    params.mobilityList = muList
+    
+    # 5) Dump to screen
+    params.print()
diff --git a/psaapProperties_6Species_5Torr_Expanded.py b/psaapProperties_6Species_5Torr_Expanded.py
new file mode 100755
index 000000000..b6ec32c71
--- /dev/null
+++ b/psaapProperties_6Species_5Torr_Expanded.py
@@ -0,0 +1,549 @@
+import numpy as np
+from scipy.interpolate import CubicSpline
+import csv
+#import matplotlib.pyplot as plt
+#import matplotlib.colors as mcolors
+import h5py as h5
+
+import logging
+
+class Reaction(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Diffusivity(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Mobility(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+
+def setPsaapProperties_6Species_5Torr_Expanded(gam, inputV0, inputVDC, params, Nr, iSample):
+    """Sets non-dimensional properties corresponding to Liu 2014 paper.
+
+    Inputs:
+      gam : Secondary electron emission coefficient
+      params : chebSolver.modelParams class
+
+    Outputs: None
+      params data is overwritten using values from Liu 2014.
+    """
+    ###################################################################
+    # User specified parameters (you may change these if you wish to
+    # run a different scenario from Liu 2014)
+    ###################################################################
+
+    # densities
+    nAr = 1.61e23     # background number density of Ar [1/m^3] (corresponds to p = 5 Torr)
+    np0 = 8e16        # "nominal" electron density [1/m^3]
+
+    # masses
+    # me = 9.10938356e-31        # mass of an electron [kg]
+    # me = 5.489e-4              # mass of an electron [u]
+    me = 0.511e6                 # mass of an electron [eV/c2]
+    # mAr = 39.948               # mass of an argon atom [u]
+    # mAr = 39.948 * 1.66054e-27 # mass of an argon atom [kg]
+    mAr = 37.2158e9              # mass of an argon atom [eV/c2]
+    # u = 931.4941e6             # eV/c2
+    c = 299792458                # speed of light [m/s]
+    se = 40                      # momentum cross section [A^2]
+
+    # nominal electron energy
+    e0 = 1.0  # [eV]
+
+    # pressure
+    p  = 666.6*1.5      # [J/m^3] *1.5 to convert it to energy (5 Torr)
+
+    # gas energy at the wall
+    Tg0 = 0.038778    # 3/2*300K*kB ~ (p0 - nT[:,0])/ntot
+
+    # characteristics of driving voltage
+    V0  = inputV0                 # amplitude of driving voltage [V]
+    verticalShift = inputVDC      # DC voltage (vertical shift in driving voltage)
+    tau = (1./13.56e6)             # period of driving voltage [s]
+    L   = 2.00*0.005              # half-gap-width [m] (gap width is 2 cm)
+    electrodeArea = np.pi*0.05**2 # electrode area [m^2] (electrode diameter = 0.1 m)
+
+    # transport parameters
+    nmue = 9.66e21   # argon number density times electron mobility [1/(V*cm*s)]
+    nmum = 0.0
+    nmur = 0.0
+    nmu4p = 0.0
+    nmui = 8.0e19
+    nDe  = 3.86e22   # argon number density times electron diffusivity [1/(cm*s)]
+    nDi  = 2.07e18   # argon number density times ion diffusivity [1/(cm*s)]
+    nDm  = 2.42e18   # argon number density times AR(m) diffusivity [1/(cm*s)]
+    nDr  = 2.42e18
+    nD4p = 2.42e18
+
+    # reaction parameters (NB: k_i = Ck*Ee^B*exp(-A/Ee))
+    #                          Ee = 3/2*Te (Te in eV)
+    #                          -> k_i = [Ck*(2/3)^B] * Ee^B * exp[-(3/2)*A/Ee]
+    # nominal
+    Ck = np.array([2.0e-13,2.1e-15,5.0e-16,6.4e-16,2.1e-21,1.32e8,1.72e7,1.50e7,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0e-18,4.0e-19,0.0,0.0,0.0,0.0,2.5e-17,2.5e-17,1.0e-15,1.0e-15,0.0,0.0]) # pre-exponential factors [m^3/s]
+    B  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-0.5,0,0,0,0,0,0,0,0,0,0]) # Temperature Power
+    A  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]) # activation temperature [eV]
+    dH = np.array([0.0,-7.541,-10.577,-7.393,0.0,0.0,0.0,0.0,11.577,11.725,13.168,15.76,-11.577,4.183,0.148,1.592,2.592,-1.444,-1.592,-11.725,-0.148,1.444,0.0,0.0,-4.183,-4.035,-2.592,-15.76,0.0,0.0,-8.985,-9.133,4.035,-13.168]) # energy lost per electron due to ionization rxn [eV]
+    dEps = np.array([0.0,15.76,11.577,11.725,13.168,0.0]) # E, AR+, AR(m), AR(r), AR(4p), AR
+
+    # BC parameters
+    # ks = 1.19e7  # electron recombination rate [cm/s]
+    ks = 1.366109824889323e7 # electron recombination rate [cm/s/eV]
+
+    ###################################################################
+    # Constants of nature (probably shouldn't change unless you have
+    # root privileges on universe)
+    ###################################################################
+    qe   = 1.6e-19   # unit charge [C]
+    eps0 = 8.86e-12  # permittivity of free space [F/m]
+    kB   = 1.38e-23  # Boltzmann constant [J/K]
+    # kB   = 8.62e-5 # Boltzmann constant [eV/K]
+
+
+    ###################################################################
+    # Calculate non-dimensional parameters
+    ###################################################################
+
+    # 1) Convert input units to base SI (except eV)
+    nDe  *= 100. # 1/(m*s)
+    nDi  *= 100. # 1/(m*s)
+    nDm  *= 100.
+    nDr  *= 100.
+    nD4p *= 100.
+    nmue *= 100. # 1/(V*m*s)
+    nmui *= 100. # 1/(V*m*s)
+    ks   *= 0.01 # m/s
+    se   *= 1.0e-20  # m^2
+
+    # 2) Compute "raw" transport parameters
+    De  = nDe/nAr
+    Di  = nDi/nAr
+    Dm  = nDm/nAr
+    Dr  = nDr/nAr
+    D4p = nD4p/nAr
+
+    mue = nmue/nAr
+    mui = nmui/nAr
+    mum = nmum/nAr
+    mur = nmur/nAr
+    mu4p = nmu4p/nAr
+
+    # 3) Compute non-dimensional properties required by solver
+    De    = De*tau/(L*L)
+    Di    = Di*tau/(L*L)
+    Dm    = Dm*tau/(L*L)
+    Dr    = Dr*tau/(L*L)
+    D4p   = D4p*tau/(L*L)
+
+    mue   = mue*V0*tau/(L*L)
+    mui   = mui*V0*tau/(L*L)
+    mum   = mum*V0*tau/(L*L)
+    mur   = mur*V0*tau/(L*L)
+    mu4p  = mu4p*V0*tau/(L*L)
+
+    Ck[0:4]  *= tau*np0
+    Ck[4]    *= tau*nAr
+    Ck[5:8]  *= tau
+    Ck[8:12] *= tau*nAr
+    Ck[12:24]  *= tau*np0
+    Ck[24:28] *= tau*np0*np0
+    Ck[28:30] *= tau*nAr
+    Ck[30:] *= tau*np0
+    A        = A*1.5/e0  # 1.5 to convert from temperature to energy
+    dH       = dH/e0
+    qStar    = V0/e0 # qe*V0/e0, since e0 in eV, need qe*V0 in eV, which is just V0 in V
+    alpha    = qe*np0*L*L/(V0*eps0)
+    ks       = ks*tau/L
+    p0       = p/qe/np0
+    kappaB   = 4.878171165833662*1.6129 # non-dimensional thermal conductivity of background specie
+                                # (2/3)*tau/L**2*Kb/np0/kB,
+                                # where Kb is the thermal conductivity of background specie
+
+    params.beta = np.array([[0,1,1,1,0,0,0,0,1,1,1,2,1,2,1,1,2,1,1,1,1,1,0,0,1,1,1,1,0,0,1,1,2,1],                     # E
+                            [0,1,1,1,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0],                     # AR+
+                            [0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,1,0,0,0,0,0],                     # AR(m)
+                            [0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0],                     # AR(r)
+                            [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0],                     # AR(4p)
+                            [2,1,1,1,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1]], dtype=np.int64)    # AR
+
+    params.alfa = np.array([[0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,0,0,0,0,1,1],                     # E
+                            [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0],                     # AR+
+                            [2,1,0,2,1,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],                     # AR(m)
+                            [0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,1,0],                     # AR(r)
+                            [0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,0,1],                     # AR(4p)
+                            [0,0,0,0,1,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0]], dtype=np.int64)    # AR
+    
+    # Rxn1:  2AR(m)         ->   2AR
+    # Rxn2:  AR(m) + AR(r)  ->   E + AR+ + AR
+    # Rxn3:  2AR(4p)        ->   E + AR+ + AR
+    # Rxn4:  2AR(m)         ->   E + AR+ + AR
+    # Rxn5:  AR(m) + AR     ->   2AR
+    # Rxn6:  AR(r)          ->   AR + hv
+    # Rxn7:  AR(4p)         ->   AR(m) + hv
+    # Rxn8:  AR(4p)         ->   AR(r) + hv
+    # Rxn9:  E + AR         ->   E + AR(m)
+    # Rxn10: E + AR         ->   E + AR(r)
+    # Rxn11: E + AR         ->   E + AR(4p)
+    # Rxn12: E + AR         ->   2E + AR+
+    # Rxn13: E + AR(m)      ->   E + AR
+    # Rxn14: E + AR(m)      ->   2E + AR+
+    # Rxn15: E + AR(m)      ->   E + AR(r)
+    # Rxn16: E + AR(m)      ->   E + AR(4p)
+    # Rxn17: E + AR(4p)     ->   2E + AR+
+    # Rxn18: E + AR(4p)     ->   E + AR(r)
+    # Rxn19: E + AR(4p)     ->   E + AR(m)
+    # Rxn20: E + AR(r)      ->   E + AR
+    # Rxn21: E + AR(r)      ->   E + AR(m)
+    # Rxn22: E + AR(r)      ->   E + AR(4p)
+    # Rxn23: E + AR+        ->   AR(m) + hv
+    # Rxn24: E + AR+        ->   AR(4p) + hv
+    # Rxn25: 2E + AR+       ->   E + AR(m)
+    # Rxn26: 2E + AR+       ->   E + AR(r)
+    # Rxn27: 2E + AR+       ->   E + AR(4p)
+    # Rxn28: 2E + AR+       ->   E + AR
+    # Rxn29: AR + AR(4p)    ->   AR + AR(m)
+    # Rxn30: AR + AR(4p)    ->   AR + AR(r)
+    # Rxn31: AR(m) + AR(4p) ->   E + AR+ + AR
+    # Rxn32: AR(r) + AR(4p) ->   E + AR+ + AR
+    # Rxn33: E + AR(r)      ->   2E + AR+
+    # Rxn34: E + AR(4p)     ->   E + AR
+
+    rxnNameDict = { 0: "2Ar(m) => 2Ar",
+                    1: "Ar(m) + Ar(r) => E + Ar+ + Ar",
+                    2: "2Ar(4p) => E + Ar+ + Ar",
+                    3: "2Ar(m) => E + Ar+ + Ar",
+                    4: "Ar(m) + Ar => 2Ar",
+                    5: "Ar(r) => Ar",
+                    6: "Ar(4p) => Ar(m)",
+                    7: "Ar(4p) => Ar(r)",
+                    8: "Excitation_Metastable",
+                    9: "Excitation_Resonant",
+                   10: "Excitation_4p",
+                   11: "Ionization",
+                   12: "DeExcitation_Metastable",
+                   13: "StepIonization_Metastable",
+                   14: "E + Ar(m) => E + Ar(r)",
+                   15: "E + Ar(m) => E + Ar(4p)",
+                   16: "StepIonization_4p",
+                   17: "E + Ar(4p) => E + Ar(r)",
+                   18: "E + Ar(4p) => E + Ar(m)",
+                   19: "DeExcitation_Resonant",
+                   20: "E + Ar(r) => E + Ar(m)",
+                   21: "E + Ar(r) => E + Ar(4p)",
+                   22: "E + Ar+ => Ar(m)",
+                   23: "E + Ar+ => Ar(4p)",
+                   24: "3BdyRecomb_Metastable",
+                   25: "3BdyRecomb_Resonant",
+                   26: "3BdyRecomb_4p",
+                   27: "3BdyRecomb_Ground",
+                   28: "Ar + Ar(4p) => Ar + Ar(m)",
+                   29: "Ar + Ar(4p) => Ar + Ar(r)",
+                   30: "Ar(m) + Ar(4p) => E + Ar+ + Ar",
+                   31: "Ar(r) + Ar(4p) => E + Ar+ + Ar",
+                   32: "StepIonization_Resonant",
+                   33: "DeExcitation_4p"}
+
+    # 4) Set values in params class
+    params.D[0]    = De
+    params.D[1]    = Di
+    params.D[2]    = Dm
+    params.D[3]    = Dr
+    params.D[4]    = D4p
+
+    params.mu[0]   = mue
+    params.mu[1]   = mui
+    params.mu[2]   = mum
+    params.mu[3]   = mur
+    params.mu[4]   = mu4p
+
+    params.A[:]    = Ck[:]
+    params.B[:]    = B[:]
+    params.C[:]    = A[:]
+
+    # Account for the 2/3 term to convert from electron temperature to electron energy
+    for i in range(len(params.A)):
+        params.A[i] *= (2/3)**(params.B[i])
+
+    params.dH[:]   = dH[:]
+    params.dEps[:] = dEps[:]
+    params.qStar   = qStar
+    params.alpha   = alpha
+    params.ks      = ks
+    params.gam     = gam
+    params.kappaB  = kappaB
+    params.nAronp0 = nAr / np0
+    params.p0      = p0
+    params.Tg0     = Tg0
+    params.EC      = 2.0 * me / mAr \
+        * np.sqrt(16.0 * (me + mAr) * e0 * c**2
+                  / (3.0 * np.pi * me * mAr)) * se * nAr * tau
+    # params.EC = 2.0 * me / mAr * 3.8e9 * tau
+
+    params.verticalShift = verticalShift / V0
+
+    # Parameters needed to compute the current with dimensions
+    params.V0Ltau  = V0 / (L * tau)
+    params.V0L     = V0 / L
+    params.LLV0tau = (L*L) / (V0*tau)
+    params.tauL    = L / tau
+    params.np0     = np0           # "nominal" electron density [1/m^3]
+    params.qe      = qe            # unit charge [C]
+    params.eps0    = eps0          # unit charge [C]
+    params.eArea   = electrodeArea # electrode area [m^2]
+
+
+    reactionExpressionslist = [f"{params.A[0]} * energy**{params.B[0]} * np.exp(-{params.C[0]} / energy)",
+                               f"{params.A[1]} * energy**{params.B[1]} * np.exp(-{params.C[1]} / energy)",
+                               f"{params.A[2]} * energy**{params.B[2]} * np.exp(-{params.C[2]} / energy)",
+                               f"{params.A[3]} * energy**{params.B[3]} * np.exp(-{params.C[3]} / energy)",
+                               f"{params.A[4]} * energy**{params.B[4]} * np.exp(-{params.C[4]} / energy)",
+                               f"{params.A[5]} * energy**{params.B[5]} * np.exp(-{params.C[5]} / energy)",
+                               f"{params.A[6]} * energy**{params.B[6]} * np.exp(-{params.C[6]} / energy)",
+                               f"{params.A[7]} * energy**{params.B[7]} * np.exp(-{params.C[7]} / energy)",
+                               f"{params.A[8]} * energy**{params.B[8]} * np.exp(-{params.C[8]} / energy)",
+                               f"{params.A[9]} * energy**{params.B[9]} * np.exp(-{params.C[9]} / energy)",
+                               f"{params.A[10]} * energy**{params.B[10]} * np.exp(-{params.C[10]} / energy)",
+                               f"{params.A[11]} * energy**{params.B[11]} * np.exp(-{params.C[11]} / energy)",
+                               f"{params.A[12]} * energy**{params.B[12]} * np.exp(-{params.C[12]} / energy)",
+                               f"{params.A[13]} * energy**{params.B[13]} * np.exp(-{params.C[13]} / energy)",
+                               f"{params.A[14]} * energy**{params.B[14]} * np.exp(-{params.C[14]} / energy)",
+                               f"{params.A[15]} * energy**{params.B[15]} * np.exp(-{params.C[15]} / energy)",
+                               f"{params.A[16]} * energy**{params.B[16]} * np.exp(-{params.C[16]} / energy)",
+                               f"{params.A[17]} * energy**{params.B[17]} * np.exp(-{params.C[17]} / energy)",
+                               f"{params.A[18]} * energy**{params.B[18]} * np.exp(-{params.C[18]} / energy)",
+                               f"{params.A[19]} * energy**{params.B[19]} * np.exp(-{params.C[19]} / energy)",
+                               f"{params.A[20]} * energy**{params.B[20]} * np.exp(-{params.C[20]} / energy)",
+                               f"{params.A[21]} * energy**{params.B[21]} * np.exp(-{params.C[21]} / energy)",
+                               f"{params.A[22]} * energy**{params.B[22]} * np.exp(-{params.C[22]} / energy)",
+                               f"{params.A[23]} * energy**{params.B[23]} * np.exp(-{params.C[23]} / energy)",
+                               f"{params.A[24]} * energy**{params.B[24]} * np.exp(-{params.C[24]} / energy)",
+                               f"{params.A[25]} * energy**{params.B[25]} * np.exp(-{params.C[25]} / energy)",
+                               f"{params.A[26]} * energy**{params.B[26]} * np.exp(-{params.C[26]} / energy)",
+                               f"{params.A[27]} * energy**{params.B[27]} * np.exp(-{params.C[27]} / energy)",
+                               f"{params.A[28]} * energy**{params.B[28]} * np.exp(-{params.C[28]} / energy)",
+                               f"{params.A[29]} * energy**{params.B[29]} * np.exp(-{params.C[29]} / energy)",
+                               f"{params.A[30]} * energy**{params.B[30]} * np.exp(-{params.C[30]} / energy)",
+                               f"{params.A[31]} * energy**{params.B[31]} * np.exp(-{params.C[31]} / energy)",
+                               f"{params.A[32]} * energy**{params.B[32]} * np.exp(-{params.C[32]} / energy)",
+                               f"{params.A[33]} * energy**{params.B[33]} * np.exp(-{params.C[33]} / energy)"]
+
+
+    reactionTExpressionslist = [f"{params.A[0]} * (energy**({params.B[0]}-1)) * np.exp(-{params.C[0]}/energy) * ({params.B[0]} + {params.C[0]}/energy)",
+                                f"{params.A[1]} * (energy**({params.B[1]}-1)) * np.exp(-{params.C[1]}/energy) * ({params.B[1]} + {params.C[1]}/energy)",
+                                f"{params.A[2]} * (energy**({params.B[2]}-1)) * np.exp(-{params.C[2]}/energy) * ({params.B[2]} + {params.C[2]}/energy)",
+                                f"{params.A[3]} * (energy**({params.B[3]}-1)) * np.exp(-{params.C[3]}/energy) * ({params.B[3]} + {params.C[3]}/energy)",
+                                f"{params.A[4]} * (energy**({params.B[4]}-1)) * np.exp(-{params.C[4]}/energy) * ({params.B[4]} + {params.C[4]}/energy)",
+                                f"{params.A[5]} * (energy**({params.B[5]}-1)) * np.exp(-{params.C[5]}/energy) * ({params.B[5]} + {params.C[5]}/energy)",
+                                f"{params.A[6]} * (energy**({params.B[6]}-1)) * np.exp(-{params.C[6]}/energy) * ({params.B[6]} + {params.C[6]}/energy)",
+				f"{params.A[7]} * (energy**({params.B[7]}-1)) * np.exp(-{params.C[7]}/energy) * ({params.B[7]} + {params.C[7]}/energy)",
+		                f"{params.A[8]} * (energy**({params.B[8]}-1)) * np.exp(-{params.C[8]}/energy) * ({params.B[8]} + {params.C[8]}/energy)",
+                                f"{params.A[9]} * (energy**({params.B[9]}-1)) * np.exp(-{params.C[9]}/energy) * ({params.B[9]} + {params.C[9]}/energy)",
+                                f"{params.A[10]} * (energy**({params.B[10]}-1)) * np.exp(-{params.C[10]}/energy) * ({params.B[10]} + {params.C[10]}/energy)",
+                                f"{params.A[11]} * (energy**({params.B[11]}-1)) * np.exp(-{params.C[11]}/energy) * ({params.B[11]} + {params.C[11]}/energy)",
+                                f"{params.A[12]} * (energy**({params.B[12]}-1)) * np.exp(-{params.C[12]}/energy) * ({params.B[12]} + {params.C[12]}/energy)",
+                                f"{params.A[13]} * (energy**({params.B[13]}-1)) * np.exp(-{params.C[13]}/energy) * ({params.B[13]} + {params.C[13]}/energy)",
+                                f"{params.A[14]} * (energy**({params.B[14]}-1)) * np.exp(-{params.C[14]}/energy) * ({params.B[14]} + {params.C[14]}/energy)",
+                                f"{params.A[15]} * (energy**({params.B[15]}-1)) * np.exp(-{params.C[15]}/energy) * ({params.B[15]} + {params.C[15]}/energy)",
+                                f"{params.A[16]} * (energy**({params.B[16]}-1)) * np.exp(-{params.C[16]}/energy) * ({params.B[16]} + {params.C[16]}/energy)",
+                                f"{params.A[17]} * (energy**({params.B[17]}-1)) * np.exp(-{params.C[17]}/energy) * ({params.B[17]} + {params.C[17]}/energy)",
+                                f"{params.A[18]} * (energy**({params.B[18]}-1)) * np.exp(-{params.C[18]}/energy) * ({params.B[18]} + {params.C[18]}/energy)",
+                                f"{params.A[19]} * (energy**({params.B[19]}-1)) * np.exp(-{params.C[19]}/energy) * ({params.B[19]} + {params.C[19]}/energy)",
+                                f"{params.A[20]} * (energy**({params.B[20]}-1)) * np.exp(-{params.C[20]}/energy) * ({params.B[20]} + {params.C[20]}/energy)",
+                                f"{params.A[21]} * (energy**({params.B[21]}-1)) * np.exp(-{params.C[21]}/energy) * ({params.B[21]} + {params.C[21]}/energy)",
+                                f"{params.A[22]} * (energy**({params.B[22]}-1)) * np.exp(-{params.C[22]}/energy) * ({params.B[22]} + {params.C[22]}/energy)",
+                                f"{params.A[23]} * (energy**({params.B[23]}-1)) * np.exp(-{params.C[23]}/energy) * ({params.B[23]} + {params.C[23]}/energy)",
+                                f"{params.A[24]} * (energy**({params.B[24]}-1)) * np.exp(-{params.C[24]}/energy) * ({params.B[24]} + {params.C[24]}/energy)",
+                                f"{params.A[25]} * (energy**({params.B[25]}-1)) * np.exp(-{params.C[25]}/energy) * ({params.B[25]} + {params.C[25]}/energy)",
+                                f"{params.A[26]} * (energy**({params.B[26]}-1)) * np.exp(-{params.C[26]}/energy) * ({params.B[26]} + {params.C[26]}/energy)",
+                                f"{params.A[27]} * (energy**({params.B[27]}-1)) * np.exp(-{params.C[27]}/energy) * ({params.B[27]} + {params.C[27]}/energy)",
+                                f"{params.A[28]} * (energy**({params.B[28]}-1)) * np.exp(-{params.C[28]}/energy) * ({params.B[28]} + {params.C[28]}/energy)",
+                                f"{params.A[29]} * (energy**({params.B[29]}-1)) * np.exp(-{params.C[29]}/energy) * ({params.B[29]} + {params.C[29]}/energy)",
+                                f"{params.A[30]} * (energy**({params.B[30]}-1)) * np.exp(-{params.C[30]}/energy) * ({params.B[30]} + {params.C[30]}/energy)",
+                                f"{params.A[31]} * (energy**({params.B[31]}-1)) * np.exp(-{params.C[31]}/energy) * ({params.B[31]} + {params.C[31]}/energy)",
+                                f"{params.A[32]} * (energy**({params.B[32]}-1)) * np.exp(-{params.C[32]}/energy) * ({params.B[32]} + {params.C[32]}/energy)",
+                                f"{params.A[33]} * (energy**({params.B[33]}-1)) * np.exp(-{params.C[33]}/energy) * ({params.B[33]} + {params.C[33]}/energy)"]
+           
+
+    reactionExpressionTypelist =  np.array([False,False,False,False,False,False,False,False, # Rxns 1-8
+                                           True,True,True,True,True,True,True,True,True,True,True,True,True,True, # Rxns 9-21
+                                           False,False,True,True,True,True,False,False,False,False,True,True]) # Rxns 22-34
+
+    thresholded_rxn = np.array([False, False, False, False, False, False, False, False,
+                                True, True, True, True, False, True, True, True, True, False, False, False, False,
+                                True, False, False, False, False, False, False, False, False, False, False, True,
+                                False])
+    reactionsList = []
+    LOGFilename = 'interpolationSample.log'
+    f = open(LOGFilename, 'w')
+
+    for i in range(Nr):
+        if reactionExpressionTypelist[i]:
+            if i < 14 or i == 16 or i == 19 or i > 23:
+                f = h5.File("../../../BOLSIGChemistry_NominalRates/{0:s}.h5".format(rxnNameDict[i]), 'r')
+                dataset = f["table"]
+            else:
+                f = h5.File("../../../BOLSIGChemistry_NominalRates/StepExcitation.h5", 'r')
+                dataset = f[rxnNameDict[i]]
+
+            Te = dataset[:,0]
+            Te /= 11604
+            rateCoeff = dataset[:,1]
+            if i > 23 and i < 28:
+                rateCoeff /= 6.022e23**2
+            else:
+                rateCoeff /= 6.022e23
+
+            ## Removing BOLSIG failures
+            fail_inds = []
+            for j in range(len(rateCoeff)):
+                if rateCoeff[j] == 0.0 and j > np.nonzero(rateCoeff)[0][0]:
+                    fail_inds.append(j)
+
+            Te = np.delete(Te, fail_inds)
+            rateCoeff = np.delete(rateCoeff, fail_inds)
+
+            # Sorting mean energy array and rate coefficient array based on
+            # the mean energy array.
+            Teinds = Te.argsort()
+            rateCoeff = rateCoeff[Teinds]
+            Te = Te[Teinds]
+
+            # Find duplicates
+            TeDuplicateinds = np.where(np.abs(np.diff(Te, axis=0)) > 0.0)
+            TeDuplicateindsForLog = np.where(np.abs(np.diff(Te, axis=0)) == 0.0)
+            rateCoeff = rateCoeff[TeDuplicateinds]
+            Te = Te[TeDuplicateinds]
+
+            # Nondimensionalization of mean energy.
+            Te *= 1.5
+
+            # Find first non-zero value of the coefficient rate.
+            I = np.nonzero(rateCoeff)
+
+            diffRateCoeff = [j-i for i, j in zip(rateCoeff[:-1], rateCoeff[1:])]
+            diffTe = [j-i for i, j in zip(Te[:-1], Te[1:])]
+
+            Monotonicity = np.asarray([j/i for i, j in zip(diffTe, diffRateCoeff)])
+            Monotonicity = np.insert(Monotonicity, 0, 0.0, axis=0)
+
+            Nan = np.isnan(Monotonicity)
+            Inf = np.isinf(Monotonicity)
+            if thresholded_rxn[i] == True:
+                indexPositive = np.where(Monotonicity>0.0)
+            else:
+                indexPositive = np.where(Monotonicity<0.0)
+            Positive = np.full(Monotonicity.shape, False, dtype=bool)
+            Positive[indexPositive] = True
+            
+            indices = Nan + Inf + Positive
+
+
+            #lastFalse = np.where(indices==False)[-1][-1] + 2
+            for k in range(len(Te)):
+               if Te[k] < 4.5 and indices[k] == False:
+                  lastFalse = k + 2
+
+            # Transformation to log scale.
+            TeLog = np.log(Te)
+
+            # Compute the slope of the rate coefficient between its first two non-zero values.
+            # Finite differences are used.
+            dydx = (rateCoeff[lastFalse + 1] - rateCoeff[lastFalse]) \
+                 / (Te[lastFalse + 1] - Te[lastFalse])
+
+            # Arrhenius form: kf = A * exp(-C / Te)
+            C = Te[lastFalse]**2.0*dydx / rateCoeff[lastFalse]
+
+            # Compute pre-exponential coefficient, A, in log scale.
+            ALog = np.log(rateCoeff[lastFalse]) + C / Te[lastFalse]
+
+            # Transform rate coefficient in log scale.
+            rateCoeffLog = np.zeros(rateCoeff.shape)
+            rateCoeffLog[lastFalse:] = np.log(rateCoeff[lastFalse:])
+            # For the troublesome values, we use the Arrhenius form.
+            rateCoeffLog[0:lastFalse] = ALog - C / Te[0:lastFalse]
+            # Nondimensionalization in log scale.
+            if (i < 12):
+                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
+            elif i > 23 and i < 28:
+                rateCoeffLog += - np.log(1.0/tau) + 2*np.log(np0)
+            else:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(np0)
+
+            # Interpolation in log scale.
+            reactionExpressionsLog = CubicSpline(TeLog, rateCoeffLog)
+            # Gradient in log scale
+            reactionTExpressionsLog = CubicSpline.derivative(reactionExpressionsLog)
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf_log = reactionExpressionsLog,
+                                kf_T_log = reactionTExpressionsLog)
+            reactionsList.append(reaction)
+
+            logging.basicConfig(filename=LOGFilename)
+            logging.warning('Interpolation info (Reaction %s):', i + 1)
+            logging.warning('First non-zero entry in the rate coefficient: %s', I[0][0])
+            logging.warning('Monotonicity of the rate coefficient start from entry: %s', lastFalse)
+            logging.warning('Position of possible duplicates in mean energy array: %s',
+                          TeDuplicateindsForLog[0])
+
+        else:
+            rxn   = eval("lambda energy :" + reactionExpressionslist[i])
+            rxn_T = eval("lambda energy :" + reactionTExpressionslist[i])
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf = rxn, kf_T = rxn_T)
+            reactionsList.append(reaction)
+
+    params.reactionsList = reactionsList
+
+    ## Electron Transport Data
+    diffList = []
+    transport = h5.File("../../../BOLSIGChemistry_NominalRates/nominal_transport.h5", 'r')
+    NDe_v_Te = transport["diffusivity"]
+    Te_trans = NDe_v_Te[:,0]
+    Te_trans /= 11604
+    De_interp = (NDe_v_Te[:,1]/nAr)*tau/(L*L)
+    De_spline = CubicSpline(Te_trans, De_interp)
+    De_Te_spline = CubicSpline.derivative(De_spline)
+    diffusivity = Diffusivity(interpolate = True, D_expression = De_spline, D_T_expression = De_Te_spline)
+    diffList.append(diffusivity)
+
+    Ns = 6
+    for i in range(1, Ns):
+        diffList.append(Diffusivity(interpolate = False))
+
+    params.diffusivityList = diffList
+
+    muList = []
+    Nmue_v_Te = transport["mobility"]
+    mue_interp = (Nmue_v_Te[:,1]/nAr)*V0*tau/(L*L)
+    mue_spline = CubicSpline(Te_trans, mue_interp)
+    mue_Te_spline = CubicSpline.derivative(mue_spline)
+    mobility = Mobility(interpolate = True, mu_expression = mue_spline, mu_T_expression = mue_Te_spline)
+    muList.append(mobility)
+
+    Ns = 6
+    for i in range(1, Ns):
+        muList.append(Mobility(interpolate = False))
+
+    params.mobilityList = muList
+    
+    # 5) Dump to screen
+    params.print()
diff --git a/psaapProperties_6Species_Expanded.py b/psaapProperties_6Species_Expanded.py
new file mode 100755
index 000000000..c5535dace
--- /dev/null
+++ b/psaapProperties_6Species_Expanded.py
@@ -0,0 +1,539 @@
+import numpy as np
+from scipy.interpolate import CubicSpline
+import csv
+#import matplotlib.pyplot as plt
+#import matplotlib.colors as mcolors
+import h5py as h5
+
+import logging
+
+class Reaction(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Diffusivity(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Mobility(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+
+def setPsaapProperties_6Species_Expanded(gam, inputV0, inputVDC, params, Nr, iSample):
+    """Sets non-dimensional properties corresponding to Liu 2014 paper.
+
+    Inputs:
+      gam : Secondary electron emission coefficient
+      params : chebSolver.modelParams class
+
+    Outputs: None
+      params data is overwritten using values from Liu 2014.
+    """
+    ###################################################################
+    # User specified parameters (you may change these if you wish to
+    # run a different scenario from Liu 2014)
+    ###################################################################
+
+    # densities
+    nAr = 3.22e22     # background number density of Ar [1/m^3] (corresponds to p=100 mTorr)
+    np0 = 8e16        # "nominal" electron density [1/m^3]
+
+    # masses
+    # me = 9.10938356e-31        # mass of an electron [kg]
+    # me = 5.489e-4              # mass of an electron [u]
+    me = 0.511e6                 # mass of an electron [eV/c2]
+    # mAr = 39.948               # mass of an argon atom [u]
+    # mAr = 39.948 * 1.66054e-27 # mass of an argon atom [kg]
+    mAr = 37.2158e9              # mass of an argon atom [eV/c2]
+    # u = 931.4941e6             # eV/c2
+    c = 299792458                # speed of light [m/s]
+    se = 40                      # momentum cross section [A^2]
+
+    # nominal electron energy
+    e0 = 1.0  # [eV]
+
+    # pressure
+    p  = 133.3*1.5      # [J/m^3] *1.5 to convert it to energy (1 Torr)
+
+    # gas energy at the wall
+    Tg0 = 0.038778    # 3/2*300K*kB ~ (p0 - nT[:,0])/ntot
+
+    # characteristics of driving voltage
+    V0  = inputV0                 # amplitude of driving voltage [V]
+    verticalShift = inputVDC      # DC voltage (vertical shift in driving voltage)
+    tau = (1./13.56e6)             # period of driving voltage [s]
+    L   = 2.00*0.005              # half-gap-width [m] (gap width is 2 cm)
+    electrodeArea = np.pi*0.05**2 # electrode area [m^2] (electrode diameter = 0.1 m)
+
+    # transport parameters
+    nmue = 9.66e21   # argon number density times electron mobility [1/(V*cm*s)]
+    nmum = 0.0
+    nmur = 0.0
+    nmu4p = 0.0
+    nmui = 8.0e19
+    nDe  = 3.86e22   # argon number density times electron diffusivity [1/(cm*s)]
+    nDi  = 2.07e18   # argon number density times ion diffusivity [1/(cm*s)]
+    nDm  = 2.42e18   # argon number density times AR(m) diffusivity [1/(cm*s)]
+    nDr  = 2.42e18
+    nD4p = 2.42e18
+
+    # reaction parameters (NB: k_i = Ck*Ee^B*exp(-A/Ee))
+    #                          Ee = 3/2*Te (Te in eV)
+    #                          -> k_i = [Ck*(2/3)^B] * Ee^B * exp[-(3/2)*A/Ee]
+    # nominal
+    Ck = np.array([2.0e-7,2.1e-9,5.0e-10,6.4e-10,2.1e-15,1.32e8,3.0e7,3.0e7,0.0,0.0,0.0,0.0,4.3e-10,0.0,3.7e-8,8.9e-7,1.8e-7,3.0e-7,3.0e-7,4.3e-10,9.1e-7,8.9e-7,5e-12,0.0,4e-13,0.0,0.0,5.0e-27,7.17e-27,2.5e-11,2.5e-11,1.0e-9,1.0e-9,0.0,0.0]) # pre-exponential factors [cm^3/s]
+    B  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0.74,0,0,0.0,0.61,0.0,0.0,0.74,0,0.0,0.0,0.0,-0.5,0.0,0.0,-4.5,-4.5,0.0,0.0,0.0,0.0,0.0,0.0])
+    A  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,2.61,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # activation temperature [eV]
+    dH = np.array([0.0,-7.412,-10.054,-7.336,0.0,0.0,0.0,0.0,11.548,11.624,12.907,15.76,-11.548,4.212,0.076,1.359,2.853,-1.283,-1.359,-11.624,-0.076,1.283,0.0,0.0,0.0,-4.212,-4.136,-2.853,-15.76,-1.359,-1.283,-8.695,-8.771,4.136,-12.907]) # energy lost per electron due to ionization rxn [eV]
+    dEps = np.array([0.0,15.76,11.548,11.624,12.907,0.0]) # E, AR+, AR(m), AR(r), AR(4p), AR
+
+    # BC parameters
+    # ks = 1.19e7  # electron recombination rate [cm/s]
+    ks = 1.366109824889323e7 # electron recombination rate [cm/s/eV]
+
+    ###################################################################
+    # Constants of nature (probably shouldn't change unless you have
+    # root privileges on universe)
+    ###################################################################
+    qe   = 1.6e-19   # unit charge [C]
+    eps0 = 8.86e-12  # permittivity of free space [F/m]
+    kB   = 1.38e-23  # Boltzmann constant [J/K]
+    # kB   = 8.62e-5 # Boltzmann constant [eV/K]
+
+
+    ###################################################################
+    # Calculate non-dimensional parameters
+    ###################################################################
+
+    # 1) Convert input units to base SI (except eV)
+    nDe  *= 100. # 1/(m*s)
+    nDi  *= 100. # 1/(m*s)
+    nDm  *= 100.
+    nDr  *= 100.
+    nD4p *= 100.
+    nmue *= 100. # 1/(V*m*s)
+    nmui *= 100. # 1/(V*m*s)
+    Ck[0:5] *= 1e-6 # m^3/s
+    Ck[5:8] *= 1 # 1/s
+    Ck[8:25]  *= 1e-6 # m^3/s
+    Ck[25:29] *= 1e-12 # m^6/s
+    Ck[29:] *= 1e-6 # m^3/s
+    ks   *= 0.01 # m/s
+    se   *= 1.0e-20  # m^2
+
+    # 2) Compute "raw" transport parameters
+    De  = nDe/nAr
+    Di  = nDi/nAr
+    Dm  = nDm/nAr
+    Dr  = nDr/nAr
+    D4p = nD4p/nAr
+
+    mue = nmue/nAr
+    mui = nmui/nAr
+    mum = nmum/nAr
+    mur = nmur/nAr
+    mu4p = nmu4p/nAr
+
+    # 3) Compute non-dimensional properties required by solver
+    De    = De*tau/(L*L)
+    Di    = Di*tau/(L*L)
+    Dm    = Dm*tau/(L*L)
+    Dr    = Dr*tau/(L*L)
+    D4p   = D4p*tau/(L*L)
+
+    mue   = mue*V0*tau/(L*L)
+    mui   = mui*V0*tau/(L*L)
+    mum   = mum*V0*tau/(L*L)
+    mur   = mur*V0*tau/(L*L)
+    mu4p  = mu4p*V0*tau/(L*L)
+
+    Ck[0:4]  *= tau*np0
+    Ck[4]    *= tau*nAr
+    Ck[5:8]  *= tau
+    Ck[8:12] *= tau*nAr
+    Ck[12:25]  *= tau*np0
+    Ck[25:29] *= tau*np0*np0
+    Ck[29:31] *= tau*nAr
+    Ck[31:] *= tau*np0
+    A        = A*1.5/e0  # 1.5 to convert from temperature to energy
+    dH       = dH/e0
+    qStar    = V0/e0 # qe*V0/e0, since e0 in eV, need qe*V0 in eV, which is just V0 in V
+    alpha    = qe*np0*L*L/(V0*eps0)
+    ks       = ks*tau/L
+    p0       = p/qe/np0
+    kappaB   = 4.878171165833662*1.6129 # non-dimensional thermal conductivity of background specie
+                                # (2/3)*tau/L**2*Kb/np0/kB,
+                                # where Kb is the thermal conductivity of background specie
+
+    params.beta = np.array([[0,1,1,1,0,0,0,0,1,1,1,2,1,2,1,1,2,1,1,1,1,1,0,0,0,1,1,1,1,0,0,1,1,2,1],                     # E
+                            [0,1,1,1,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0],                     # AR+
+                            [0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,1,0,0,0,1,0,0,0,0,0],                     # AR(m)
+                            [0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0],                     # AR(r)
+                            [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0],                     # AR(4p)
+                            [2,1,1,1,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,1,0,1]], dtype=np.int64)    # AR
+
+    params.alfa = np.array([[0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,0,0,0,0,1,1],                     # E
+                            [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,0,0,0,0,0,0],                     # AR+
+                            [2,1,0,2,1,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],                     # AR(m)
+                            [0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,1,1,0],                     # AR(r)
+                            [0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,1],                     # AR(4p)
+                            [0,0,0,0,1,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0]], dtype=np.int64)    # AR
+	# Rxn1:  2AR(m)         ->   2AR
+	# Rxn2:  AR(m) + AR(r)  ->   E + AR+ + AR
+	# Rxn3:  2AR(4p)        ->   E + AR+ + AR
+	# Rxn4:  2AR(m)         ->   E + AR+ + AR
+	# Rxn5:  AR(m) + AR     ->   2AR
+	# Rxn6:  AR(r)          ->   AR + hv
+	# Rxn7:  AR(4p)         ->   AR(m) + hv
+	# Rxn8:  AR(4p)         ->   AR(r) + hv
+    # Rxn9:  E + AR         ->   E + AR(m)
+    # Rxn10: E + AR         ->   E + AR(r)
+    # Rxn11: E + AR         ->   E + AR(4p)
+    # Rxn12: E + AR         ->   2E + AR+
+    # Rxn13: E + AR(m)      ->   E + AR
+    # Rxn14: E + AR(m)      ->   2E + AR+
+    # Rxn15: E + AR(m)      ->   E + AR(r)
+    # Rxn16: E + AR(m)      ->   E + AR(4p)
+    # Rxn17: E + AR(4p)     ->   2E + AR+
+    # Rxn18: E + AR(4p)     ->   E + AR(r)
+    # Rxn19: E + AR(4p)     ->   E + AR(m)
+    # Rxn20: E + AR(r)      ->   E + AR
+    # Rxn21: E + AR(r)      ->   E + AR(m)
+    # Rxn22: E + AR(r)      ->   E + AR(4p)
+    # Rxn23: E + AR+        ->   AR(m) + hv
+    # Rxn24: E + AR+        ->   AR(r) + hv
+    # Rxn25: E + AR+        ->   AR(4p) + hv
+    # Rxn26: 2E + AR+       ->   E + AR(m)
+    # Rxn27: 2E + AR+       ->   E + AR(r)
+    # Rxn28: 2E + AR+       ->   E + AR(4p)
+    # Rxn29: 2E + AR+       ->   E + AR
+    # Rxn30: AR + AR(4p)    ->   AR + AR(m)
+    # Rxn31: AR + AR(4p)    ->   AR + AR(r)
+    # Rxn32: AR(m) + AR(4p) ->   E + AR+ + AR
+    # Rxn33: AR(r) + AR(4p) ->   E + AR+ + AR
+    # Rxn34: E + AR(r)      ->   2E + AR+
+    # Rxn35: E + AR(4p)     ->   E + AR
+
+    rxnNameDict = { 0: "2Ar(m) => 2Ar",
+                    1: "Ar(m) + Ar(r) => E + Ar+ + Ar",
+                    2: "2Ar(4p) => E + Ar+ + Ar",
+                    3: "2Ar(m) => E + Ar+ + Ar",
+                    4: "Ar(m) + Ar => 2Ar",
+                    5: "Ar(r) => Ar",
+                    6: "Ar(4p) => Ar(m)",
+                    7: "Ar(4p) => Ar(r)",
+                    8: "1s-metastable",
+                    9: "1s-resonance",
+                   10: "2p-lumped",
+                   11: "Ionization",
+                   12: "Deexci-metastable",
+                   13: "StepIonization",
+                   14: "E + Ar(m) => E + Ar(r)",
+                   15: "E + Ar(m) => E + Ar(4p)",
+                   16: "StepIonization",
+                   17: "E + Ar(4p) => E + Ar(r)",
+                   18: "E + Ar(4p) => E + Ar(m)",
+                   19: "Deexci-resonance",
+                   20: "E + Ar(r) => E + Ar(m)",
+                   21: "E + Ar(r) => E + Ar(4p)",
+                   22: "E + Ar+ => Ar(m)",
+                   23: "E + Ar+ => Ar(r)",
+                   24: "E + Ar+ => Ar(4p)",
+                   25: "3BdyRecomb-metastable",
+                   26: "3BdyRecomb-metastable",
+                   27: "3BdyRecomb-metastable",
+                   28: "3BdyRecomb-ground",
+                   29: "Ar + Ar(4p) => Ar + Ar(m)",
+                   30: "Ar + Ar(4p) => Ar + Ar(r)",
+                   31: "Ar(m) + Ar(4p) => E + Ar+ + Ar",
+                   32: "Ar(r) + Ar(4p) => E + Ar+ + Ar",
+                   33: "StepIonization",
+                   34: "Deexci-2p"}
+
+    # 4) Set values in params class
+    params.D[0]    = De
+    params.D[1]    = Di
+    params.D[2]    = Dm
+    params.D[3]    = Dr
+    params.D[4]    = D4p
+
+    params.mu[0]   = mue
+    params.mu[1]   = mui
+    params.mu[2]   = mum
+    params.mu[3]   = mur
+    params.mu[4]   = mu4p
+
+    params.A[:]    = Ck[:]
+    params.B[:]    = B[:]
+    params.C[:]    = A[:]
+
+    # Account for the 2/3 term to convert from electron temperature to electron energy
+    for i in range(len(params.A)):
+        params.A[i] *= (2/3)**(params.B[i])
+
+    params.dH[:]   = dH[:]
+    params.dEps[:] = dEps[:]
+    params.qStar   = qStar
+    params.alpha   = alpha
+    params.ks      = ks
+    params.gam     = gam
+    params.kappaB  = kappaB
+    params.nAronp0 = nAr / np0
+    params.p0      = p0
+    params.Tg0     = Tg0
+    params.EC      = 2.0 * me / mAr \
+        * np.sqrt(16.0 * (me + mAr) * e0 * c**2
+                  / (3.0 * np.pi * me * mAr)) * se * nAr * tau
+    # params.EC = 2.0 * me / mAr * 3.8e9 * tau
+
+    params.verticalShift = verticalShift / V0
+
+    # Parameters needed to compute the current with dimensions
+    params.V0Ltau  = V0 / (L * tau)
+    params.V0L     = V0 / L
+    params.LLV0tau = (L*L) / (V0*tau)
+    params.tauL    = L / tau
+    params.np0     = np0           # "nominal" electron density [1/m^3]
+    params.qe      = qe            # unit charge [C]
+    params.eps0    = eps0          # unit charge [C]
+    params.eArea   = electrodeArea # electrode area [m^2]
+
+
+    reactionExpressionslist = [f"{params.A[0]} * energy**{params.B[0]} * np.exp(-{params.C[0]} / energy)",
+                               f"{params.A[1]} * energy**{params.B[1]} * np.exp(-{params.C[1]} / energy)",
+                               f"{params.A[2]} * energy**{params.B[2]} * np.exp(-{params.C[2]} / energy)",
+                               f"{params.A[3]} * energy**{params.B[3]} * np.exp(-{params.C[3]} / energy)",
+                               f"{params.A[4]} * energy**{params.B[4]} * np.exp(-{params.C[4]} / energy)",
+                               f"{params.A[5]} * energy**{params.B[5]} * np.exp(-{params.C[5]} / energy)",
+                               f"{params.A[6]} * energy**{params.B[6]} * np.exp(-{params.C[6]} / energy)",
+                               f"{params.A[7]} * energy**{params.B[7]} * np.exp(-{params.C[7]} / energy)",
+                               f"{params.A[8]} * energy**{params.B[8]} * np.exp(-{params.C[8]} / energy)",
+                               f"{params.A[9]} * energy**{params.B[9]} * np.exp(-{params.C[9]} / energy)",
+                               f"{params.A[10]} * energy**{params.B[10]} * np.exp(-{params.C[10]} / energy)",
+                               f"{params.A[11]} * energy**{params.B[11]} * np.exp(-{params.C[11]} / energy)",
+                               f"{params.A[12]} * energy**{params.B[12]} * np.exp(-{params.C[12]} / energy)",
+                               f"{params.A[13]} * energy**{params.B[13]} * np.exp(-{params.C[13]} / energy)",
+                               f"{params.A[14]} * energy**{params.B[14]} * np.exp(-{params.C[14]} / energy)",
+                               f"{params.A[15]} * energy**{params.B[15]} * np.exp(-{params.C[15]} / energy)",
+                               f"{params.A[16]} * energy**{params.B[16]} * np.exp(-{params.C[16]} / energy)",
+                               f"{params.A[17]} * energy**{params.B[17]} * np.exp(-{params.C[17]} / energy)",
+                               f"{params.A[18]} * energy**{params.B[18]} * np.exp(-{params.C[18]} / energy)",
+                               f"{params.A[19]} * energy**{params.B[19]} * np.exp(-{params.C[19]} / energy)",
+                               f"{params.A[20]} * energy**{params.B[20]} * np.exp(-{params.C[20]} / energy)",
+                               f"{params.A[21]} * energy**{params.B[21]} * np.exp(-{params.C[21]} / energy)",
+                               f"{params.A[22]} * energy**{params.B[22]} * np.exp(-{params.C[22]} / energy)",
+                               f"{params.A[23]} * energy**{params.B[23]} * np.exp(-{params.C[23]} / energy)",
+                               f"{params.A[24]} * energy**{params.B[24]} * np.exp(-{params.C[24]} / energy)",
+                               f"{params.A[25]} * energy**{params.B[25]} * np.exp(-{params.C[25]} / energy)",
+                               f"{params.A[26]} * energy**{params.B[26]} * np.exp(-{params.C[26]} / energy)",
+                               f"{params.A[27]} * energy**{params.B[27]} * np.exp(-{params.C[27]} / energy)",
+                               f"{params.A[28]} * energy**{params.B[28]} * np.exp(-{params.C[28]} / energy)",
+                               f"{params.A[29]} * energy**{params.B[29]} * np.exp(-{params.C[29]} / energy)",
+                               f"{params.A[30]} * energy**{params.B[30]} * np.exp(-{params.C[30]} / energy)",
+                               f"{params.A[31]} * energy**{params.B[31]} * np.exp(-{params.C[31]} / energy)",
+                               f"{params.A[32]} * energy**{params.B[32]} * np.exp(-{params.C[32]} / energy)",
+                               f"{params.A[33]} * energy**{params.B[33]} * np.exp(-{params.C[33]} / energy)",
+                               f"{params.A[34]} * energy**{params.B[34]} * np.exp(-{params.C[34]} / energy)"]
+
+
+    reactionTExpressionslist = [f"{params.A[0]} * (energy**({params.B[0]}-1)) * np.exp(-{params.C[0]}/energy) * ({params.B[0]} + {params.C[0]}/energy)",
+                                f"{params.A[1]} * (energy**({params.B[1]}-1)) * np.exp(-{params.C[1]}/energy) * ({params.B[1]} + {params.C[1]}/energy)",
+                                f"{params.A[2]} * (energy**({params.B[2]}-1)) * np.exp(-{params.C[2]}/energy) * ({params.B[2]} + {params.C[2]}/energy)",
+                                f"{params.A[3]} * (energy**({params.B[3]}-1)) * np.exp(-{params.C[3]}/energy) * ({params.B[3]} + {params.C[3]}/energy)",
+                                f"{params.A[4]} * (energy**({params.B[4]}-1)) * np.exp(-{params.C[4]}/energy) * ({params.B[4]} + {params.C[4]}/energy)",
+                                f"{params.A[5]} * (energy**({params.B[5]}-1)) * np.exp(-{params.C[5]}/energy) * ({params.B[5]} + {params.C[5]}/energy)",
+                                f"{params.A[6]} * (energy**({params.B[6]}-1)) * np.exp(-{params.C[6]}/energy) * ({params.B[6]} + {params.C[6]}/energy)",
+				                f"{params.A[7]} * (energy**({params.B[7]}-1)) * np.exp(-{params.C[7]}/energy) * ({params.B[7]} + {params.C[7]}/energy)",
+		                        f"{params.A[8]} * (energy**({params.B[8]}-1)) * np.exp(-{params.C[8]}/energy) * ({params.B[8]} + {params.C[8]}/energy)",
+                                f"{params.A[9]} * (energy**({params.B[9]}-1)) * np.exp(-{params.C[9]}/energy) * ({params.B[9]} + {params.C[9]}/energy)",
+                                f"{params.A[10]} * (energy**({params.B[10]}-1)) * np.exp(-{params.C[10]}/energy) * ({params.B[10]} + {params.C[10]}/energy)",
+                                f"{params.A[11]} * (energy**({params.B[11]}-1)) * np.exp(-{params.C[11]}/energy) * ({params.B[11]} + {params.C[11]}/energy)",
+                                f"{params.A[12]} * (energy**({params.B[12]}-1)) * np.exp(-{params.C[12]}/energy) * ({params.B[12]} + {params.C[12]}/energy)",
+                                f"{params.A[13]} * (energy**({params.B[13]}-1)) * np.exp(-{params.C[13]}/energy) * ({params.B[13]} + {params.C[13]}/energy)",
+                                f"{params.A[14]} * (energy**({params.B[14]}-1)) * np.exp(-{params.C[14]}/energy) * ({params.B[14]} + {params.C[14]}/energy)",
+                                f"{params.A[15]} * (energy**({params.B[15]}-1)) * np.exp(-{params.C[15]}/energy) * ({params.B[15]} + {params.C[15]}/energy)",
+                                f"{params.A[16]} * (energy**({params.B[16]}-1)) * np.exp(-{params.C[16]}/energy) * ({params.B[16]} + {params.C[16]}/energy)",
+                                f"{params.A[17]} * (energy**({params.B[17]}-1)) * np.exp(-{params.C[17]}/energy) * ({params.B[17]} + {params.C[17]}/energy)",
+                                f"{params.A[18]} * (energy**({params.B[18]}-1)) * np.exp(-{params.C[18]}/energy) * ({params.B[18]} + {params.C[18]}/energy)",
+                                f"{params.A[19]} * (energy**({params.B[19]}-1)) * np.exp(-{params.C[19]}/energy) * ({params.B[19]} + {params.C[19]}/energy)",
+                                f"{params.A[20]} * (energy**({params.B[20]}-1)) * np.exp(-{params.C[20]}/energy) * ({params.B[20]} + {params.C[20]}/energy)",
+                                f"{params.A[21]} * (energy**({params.B[21]}-1)) * np.exp(-{params.C[21]}/energy) * ({params.B[21]} + {params.C[21]}/energy)",
+                                f"{params.A[22]} * (energy**({params.B[22]}-1)) * np.exp(-{params.C[22]}/energy) * ({params.B[22]} + {params.C[22]}/energy)",
+                                f"{params.A[23]} * (energy**({params.B[23]}-1)) * np.exp(-{params.C[23]}/energy) * ({params.B[23]} + {params.C[23]}/energy)",
+                                f"{params.A[24]} * (energy**({params.B[24]}-1)) * np.exp(-{params.C[24]}/energy) * ({params.B[24]} + {params.C[24]}/energy)",
+                                f"{params.A[25]} * (energy**({params.B[25]}-1)) * np.exp(-{params.C[25]}/energy) * ({params.B[25]} + {params.C[25]}/energy)",
+                                f"{params.A[26]} * (energy**({params.B[26]}-1)) * np.exp(-{params.C[26]}/energy) * ({params.B[26]} + {params.C[26]}/energy)",
+                                f"{params.A[27]} * (energy**({params.B[27]}-1)) * np.exp(-{params.C[27]}/energy) * ({params.B[27]} + {params.C[27]}/energy)",
+                                f"{params.A[28]} * (energy**({params.B[28]}-1)) * np.exp(-{params.C[28]}/energy) * ({params.B[28]} + {params.C[28]}/energy)",
+                                f"{params.A[29]} * (energy**({params.B[29]}-1)) * np.exp(-{params.C[29]}/energy) * ({params.B[29]} + {params.C[29]}/energy)",
+                                f"{params.A[30]} * (energy**({params.B[30]}-1)) * np.exp(-{params.C[30]}/energy) * ({params.B[30]} + {params.C[30]}/energy)",
+                                f"{params.A[31]} * (energy**({params.B[31]}-1)) * np.exp(-{params.C[31]}/energy) * ({params.B[31]} + {params.C[31]}/energy)",
+                                f"{params.A[32]} * (energy**({params.B[32]}-1)) * np.exp(-{params.C[32]}/energy) * ({params.B[32]} + {params.C[32]}/energy)",
+                                f"{params.A[33]} * (energy**({params.B[33]}-1)) * np.exp(-{params.C[33]}/energy) * ({params.B[33]} + {params.C[33]}/energy)",
+                                f"{params.A[34]} * (energy**({params.B[34]}-1)) * np.exp(-{params.C[34]}/energy) * ({params.B[34]} + {params.C[34]}/energy)"]
+           
+
+    reactionExpressionTypelist =  np.array([False,False,False,False,False,False,False,False,
+                                           True,True,True,True,True,True,True,True,True,True,True,True,True,True,
+                                           False, False, False, True, True, True, True, False, False, False, False,
+                                           True, True])
+
+    reactionsList = []
+    LOGFilename = 'interpolationSample.log'
+    f = open(LOGFilename, 'w')
+
+    for i in range(Nr):
+        if reactionExpressionTypelist[i]:
+            if i < 14 or i == 16 or i == 19 or i == 25 or i == 26 or i == 27 or i == 28 or i > 32:
+                f = h5.File("../../../BOLSIGChemistry_NominalRates/{0:s}.h5".format(rxnNameDict[i]), 'r')
+                dataset = f["table"]
+            else:
+                f = h5.File("../../../BOLSIGChemistry_NominalRates/StepwiseExcitations.nominal.h5", 'r')
+                dataset = f[rxnNameDict[i]]
+
+            Te = dataset[:,0]
+            Te /= 11604
+            rateCoeff = dataset[:,1]
+            rateCoeff /= 6.022e23
+
+            # Sorting mean energy array and rate coefficient array based on
+            # the mean energy array.
+            Teinds = Te.argsort()
+            rateCoeff = rateCoeff[Teinds]
+            Te = Te[Teinds]
+
+            # Find duplicates
+            TeDuplicateinds = np.where(np.abs(np.diff(Te, axis=0)) > 0.0)
+            TeDuplicateindsForLog = np.where(np.abs(np.diff(Te, axis=0)) == 0.0)
+            rateCoeff = rateCoeff[TeDuplicateinds]
+            Te = Te[TeDuplicateinds]
+
+            # Nondimensionalization of mean energy.
+            Te *= 1.5
+
+            # Find first non-zero value of the coefficient rate.
+            I = np.nonzero(rateCoeff)
+
+            diffRateCoeff = [j-i for i, j in zip(rateCoeff[:-1], rateCoeff[1:])]
+            diffTe = [j-i for i, j in zip(Te[:-1], Te[1:])]
+
+            Monotonicity = np.asarray([j/i for i, j in zip(diffTe, diffRateCoeff)])
+            Monotonicity = np.insert(Monotonicity, 0, 0.0, axis=0)
+
+            Nan = np.isnan(Monotonicity)
+            Inf = np.isinf(Monotonicity)
+            indexPositive = np.where(Monotonicity>0.0)
+            Positive = np.full(Monotonicity.shape, False, dtype=bool)
+            Positive[indexPositive] = True
+            
+            indices = Nan + Inf + Positive
+
+
+            #lastFalse = np.where(indices==False)[-1][-1] + 2
+            for k in range(len(Te)):
+               if Te[k] < 4.5 and indices[k] == False:
+                  lastFalse = k + 2
+
+            # Transformation to log scale.
+            TeLog = np.log(Te)
+
+            # Compute the slope of the rate coefficient between its first two non-zero values.
+            # Finite differences are used.
+            dydx = (rateCoeff[lastFalse + 1] - rateCoeff[lastFalse]) \
+                 / (Te[lastFalse + 1] - Te[lastFalse])
+
+            # Arrhenius form: kf = A * exp(-C / Te)
+            C = Te[lastFalse]**2.0*dydx / rateCoeff[lastFalse]
+
+            # Compute pre-exponential coefficient, A, in log scale.
+            ALog = np.log(rateCoeff[lastFalse]) + C / Te[lastFalse]
+
+            # Transform rate coefficient in log scale.
+            rateCoeffLog = np.zeros(rateCoeff.shape)
+            rateCoeffLog[lastFalse:] = np.log(rateCoeff[lastFalse:])
+            # For the troublesome values, we use the Arrhenius form.
+            rateCoeffLog[0:lastFalse] = ALog - C / Te[0:lastFalse]
+            # Nondimensionalization in log scale.
+            if (i < 13):
+                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
+            else:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(np0)
+
+            # Interpolation in log scale.
+            reactionExpressionsLog = CubicSpline(TeLog, rateCoeffLog)
+            # Gradient in log scale
+            reactionTExpressionsLog = CubicSpline.derivative(reactionExpressionsLog)
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf_log = reactionExpressionsLog,
+                                kf_T_log = reactionTExpressionsLog)
+            reactionsList.append(reaction)
+
+            logging.basicConfig(filename=LOGFilename)
+            logging.warning('Interpolation info (Reaction %s):', i + 1)
+            logging.warning('First non-zero entry in the rate coefficient: %s', I[0][0])
+            logging.warning('Monotonicity of the rate coefficient start from entry: %s', lastFalse)
+            logging.warning('Position of possible duplicates in mean energy array: %s',
+                          TeDuplicateindsForLog[0])
+
+        else:
+            rxn   = eval("lambda energy :" + reactionExpressionslist[i])
+            rxn_T = eval("lambda energy :" + reactionTExpressionslist[i])
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf = rxn, kf_T = rxn_T)
+            reactionsList.append(reaction)
+
+    params.reactionsList = reactionsList
+
+    ## Electron Transport Data
+    diffList = []
+    transport = h5.File("../../../BOLSIGChemistry_Transport/nominal_transport.h5", 'r')
+    NDe_v_Te = transport["diffusivity"]
+    Te_trans = NDe_v_Te[:,0]
+    Te_trans /= 11604
+    print("Te_min = {0:.6e}".format(NDe_v_Te[0,0]))
+    print("Te_max = {0:.6e}".format(NDe_v_Te[-1,0]))
+    De_interp = (NDe_v_Te[:,1]/nAr)*tau/(L*L)
+    De_spline = CubicSpline(Te_trans, De_interp)
+    De_Te_spline = CubicSpline.derivative(De_spline)
+    diffusivity = Diffusivity(interpolate = True, D_expression = De_spline, D_T_expression = De_Te_spline)
+    diffList.append(diffusivity)
+
+    Ns = 6
+    for i in range(1, Ns):
+        diffList.append(Diffusivity(interpolate = False))
+
+    params.diffusivityList = diffList
+
+    muList = []
+    Nmue_v_Te = transport["mobility"]
+    mue_interp = (Nmue_v_Te[:,1]/nAr)*V0*tau/(L*L)
+    mue_spline = CubicSpline(Te_trans, mue_interp)
+    mue_Te_spline = CubicSpline.derivative(mue_spline)
+    mobility = Mobility(interpolate = True, mu_expression = mue_spline, mu_T_expression = mue_Te_spline)
+    muList.append(mobility)
+
+    Ns = 6
+    for i in range(1, Ns):
+        muList.append(Mobility(interpolate = False))
+
+    params.mobilityList = muList
+    
+    # 5) Dump to screen
+    params.print()
diff --git a/psaapProperties_6Species_ModTrans.py b/psaapProperties_6Species_ModTrans.py
new file mode 100755
index 000000000..05abf8348
--- /dev/null
+++ b/psaapProperties_6Species_ModTrans.py
@@ -0,0 +1,533 @@
+import numpy as np
+from scipy.interpolate import CubicSpline
+import csv
+#import matplotlib.pyplot as plt
+#import matplotlib.colors as mcolors
+import h5py as h5
+
+import logging
+
+class Reaction(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Diffusivity(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Mobility(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+
+def setPsaapProperties_6Species_Nominal(gam, inputV0, inputVDC, params, Nr, iSample):
+    """Sets non-dimensional properties corresponding to Liu 2014 paper.
+
+    Inputs:
+      gam : Secondary electron emission coefficient
+      params : chebSolver.modelParams class
+
+    Outputs: None
+      params data is overwritten using values from Liu 2014.
+    """
+    ###################################################################
+    # User specified parameters (you may change these if you wish to
+    # run a different scenario from Liu 2014)
+    ###################################################################
+
+    # densities
+    nAr = 3.22e22     # background number density of Ar [1/m^3] (corresponds to p=100 mTorr)
+    np0 = 8e16        # "nominal" electron density [1/m^3]
+
+    # masses
+    # me = 9.10938356e-31        # mass of an electron [kg]
+    # me = 5.489e-4              # mass of an electron [u]
+    me = 0.511e6                 # mass of an electron [eV/c2]
+    # mAr = 39.948               # mass of an argon atom [u]
+    # mAr = 39.948 * 1.66054e-27 # mass of an argon atom [kg]
+    mAr = 37.2158e9              # mass of an argon atom [eV/c2]
+    # u = 931.4941e6             # eV/c2
+    c = 299792458                # speed of light [m/s]
+    se = 40                      # momentum cross section [A^2]
+
+    # nominal electron energy
+    e0 = 1.0  # [eV]
+
+    # pressure
+    p  = 133.3*1.5      # [J/m^3] *1.5 to convert it to energy (1 Torr)
+
+    # gas energy at the wall
+    Tg0 = 0.038778    # 3/2*300K*kB ~ (p0 - nT[:,0])/ntot
+
+    # characteristics of driving voltage
+    V0  = inputV0                 # amplitude of driving voltage [V]
+    verticalShift = inputVDC      # DC voltage (vertical shift in driving voltage)
+    tau = (1./13.56e6)             # period of driving voltage [s]
+    L   = 2.00*0.005              # half-gap-width [m] (gap width is 2 cm)
+    electrodeArea = np.pi*0.05**2 # electrode area [m^2] (electrode diameter = 0.1 m)
+
+    # transport parameters
+    nmue = 9.66e21   # argon number density times electron mobility [1/(V*cm*s)]
+    nmum = 0.0
+    nmur = 0.0
+    nmu4p = 0.0
+    nmui = 8.0e19
+    nDe  = 3.86e22   # argon number density times electron diffusivity [1/(cm*s)]
+    nDi  = 2.07e18   # argon number density times ion diffusivity [1/(cm*s)]
+    nDm  = 2.42e18   # argon number density times AR(m) diffusivity [1/(cm*s)]
+    nDr  = 2.42e18
+    nD4p = 2.42e18
+
+    # reaction parameters (NB: k_i = Ck*Ee^B*exp(-A/Ee))
+    #                          Ee = 3/2*Te (Te in eV)
+    #                          -> k_i = [Ck*(2/3)^B] * Ee^B * exp[-(3/2)*A/Ee]
+    # nominal
+    Ck = np.array([2.0e-7,2.1e-9,5.0e-10,6.4e-10,2.1e-15,1.0e5,3.2e7,3.0e7,3.0e7,0.0,0.0,0.0,0.0,4.3e-10,0.0,3.7e-8,8.9e-7,1.8e-7,3.0e-7,3.0e-7,4.3e-10,9.1e-7,8.9e-7]) # pre-exponential factors [cm^3/s]
+    B  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0.74,0,0,0.51,0.61,0.51,0.51,0.74,0,0.51])
+    A  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.59,2.61,0,0,0,0,1.59]) # activation temperature [eV]
+    dH = np.array([0.0,-7.412,-10.054,-7.336,0.0,0.0,0.0,0.0,0.0,11.548,11.624,12.907,15.76,-11.548,4.212,0.076,1.359,2.853,-1.283,-1.359,-11.624,-0.076,0.983]) # energy lost per electron due to ionization rxn [eV]
+    dEps = np.array([0.0,15.76,11.548,11.624,12.907,0.0]) # E, AR+, AR(m), AR(r), AR(4p), AR
+
+    # BC parameters
+    # ks = 1.19e7  # electron recombination rate [cm/s]
+    ks = 1.366109824889323e7 # electron recombination rate [cm/s/eV]
+
+    ###################################################################
+    # Constants of nature (probably shouldn't change unless you have
+    # root privileges on universe)
+    ###################################################################
+    qe   = 1.6e-19   # unit charge [C]
+    eps0 = 8.86e-12  # permittivity of free space [F/m]
+    kB   = 1.38e-23  # Boltzmann constant [J/K]
+    # kB   = 8.62e-5 # Boltzmann constant [eV/K]
+
+
+    ###################################################################
+    # Calculate non-dimensional parameters
+    ###################################################################
+
+    # 1) Convert input units to base SI (except eV)
+    nDe  *= 100. # 1/(m*s)
+    nDi  *= 100. # 1/(m*s)
+    nDm  *= 100.
+    nDr  *= 100.
+    nD4p *= 100.
+    nmue *= 100. # 1/(V*m*s)
+    nmui *= 100. # 1/(V*m*s)
+    Ck[0:5] *= 1e-6 # m^3/s
+    Ck[5:9] *= 1 # 1/s
+    Ck[9:]  *= 1e-6 # m^3/s
+    ks   *= 0.01 # m/s
+    se   *= 1.0e-20  # m^2
+
+    # 2) Compute "raw" transport parameters
+    De  = nDe/nAr
+    Di  = nDi/nAr
+    Dm  = nDm/nAr
+    Dr  = nDr/nAr
+    D4p = nD4p/nAr
+
+    mue = nmue/nAr
+    mui = nmui/nAr
+    mum = nmum/nAr
+    mur = nmur/nAr
+    mu4p = nmu4p/nAr
+
+    # 3) Compute non-dimensional properties required by solver
+    De    = De*tau/(L*L)
+    Di    = Di*tau/(L*L)
+    Dm    = Dm*tau/(L*L)
+    Dr    = Dr*tau/(L*L)
+    D4p   = D4p*tau/(L*L)
+
+    mue   = mue*V0*tau/(L*L)
+    mui   = mui*V0*tau/(L*L)
+    mum   = mum*V0*tau/(L*L)
+    mur   = mur*V0*tau/(L*L)
+    mu4p  = mu4p*V0*tau/(L*L)
+
+    Ck[0:4]  *= tau*np0
+    Ck[4]    *= tau*nAr
+    Ck[5:9]  *= tau
+    Ck[9:13] *= tau*nAr
+    Ck[13:]  *= tau*np0
+    A        = A*1.5/e0  # 1.5 to convert from temperature to energy
+    dH       = dH/e0
+    qStar    = V0/e0 # qe*V0/e0, since e0 in eV, need qe*V0 in eV, which is just V0 in V
+    alpha    = qe*np0*L*L/(V0*eps0)
+    ks       = ks*tau/L
+    p0       = p/qe/np0
+    kappaB   = 4.878171165833662*1.6129 # non-dimensional thermal conductivity of background specie
+                                # (2/3)*tau/L**2*Kb/np0/kB,
+                                # where Kb is the thermal conductivity of background specie
+
+    params.beta = np.array([[0,1,1,1,0,0,0,0,0,1,1,1,2,1,2,1,1,2,1,1,1,1,1],                     # E
+                            [0,1,1,1,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0],                     # AR+
+                            [0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0],                     # AR(m)
+                            [0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0],                     # AR(r)
+                            [0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1],                     # AR(4p)
+                            [2,1,1,1,2,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0]], dtype=np.int64)    # AR
+
+    params.alfa = np.array([[0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1],                     # E
+                            [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],                     # AR+
+                            [2,1,0,2,1,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0],                     # AR(m)
+                            [0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1],                     # AR(r)
+                            [0,0,2,0,0,0,1,1,1,0,0,0,0,0,0,0,0,1,1,1,0,0,0],                     # AR(4p)
+                            [0,0,0,0,1,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0]], dtype=np.int64)    # AR
+	# Rxn1:  2AR(m)         ->   2AR
+	# Rxn2:  AR(m) + AR(r)  ->   E + AR+ + AR
+	# Rxn3:  2AR(4p)        ->   E + AR+ + AR
+	# Rxn4:  2AR(m)         ->   E + AR+ + AR
+	# Rxn5:  AR(m) + AR     ->   2AR
+	# Rxn6:  AR(r)          ->   AR
+	# Rxn7:  AR(4p)         ->   AR
+	# Rxn8:  AR(4p)         ->   AR(m)
+	# Rxn9:  AR(4p)         ->   AR(r)
+    # Rxn10: E + AR         ->   E + AR(m)
+    # Rxn11: E + AR         ->   E + AR(r)
+    # Rxn12: E + AR         ->   E + AR(4p)
+    # Rxn13: E + AR         ->   2E + AR+
+    # Rxn14: E + AR(m)      ->   E + AR
+    # Rxn15: E + AR(m)      ->   2E + AR+
+    # Rxn16: E + AR(m)      ->   E + AR(r)
+    # Rxn17: E + AR(m)      ->   E + AR(4p)
+    # Rxn18: E + AR(4p)     ->   2E + AR+
+    # Rxn19: E + AR(4p)     ->   E + AR(r)
+    # Rxn20: E + AR(4p)     ->   E + AR(m)
+    # Rxn21: E + AR(r)      ->   E + AR
+    # Rxn22: E + AR(r)      ->   E + AR(m)
+    # Rxn23: E + AR(r)      ->   E + AR(4p)
+
+    rxnNameDict = { 0: "2Ar(m) => 2Ar",
+                    1: "Ar(m) + Ar(r) => E + Ar+ + Ar",
+                    2: "2Ar(4p) => E + Ar+ + Ar",
+                    3: "2Ar(m) => E + Ar+ + Ar",
+                    4: "Ar(m) + Ar => 2Ar",
+                    5: "Ar(r) => Ar",
+                    6: "Ar(4p) => Ar",
+                    7: "Ar(4p) => Ar(m)",
+                    8: "Ar(4p) => Ar(r)",
+                    9: "lumped.metastable",
+                   10: "lumped.resonance",
+                   11: "lumped.2p",
+                   12: "ionization",
+                   13: "E + Ar(m) => E + Ar",
+                   14: "step_ionization",
+                   15: "E + Ar(m) => E + Ar(r)",
+                   16: "E + Ar(m) => E + Ar(4p)",
+                   17: "E + Ar(4p) => 2E + Ar+",
+                   18: "E + Ar(4p) => E + Ar(r)",
+                   19: "E + Ar(4p) => E + Ar(m)",
+                   20: "E + Ar(r) => E + Ar",
+                   21: "E + Ar(r) => E + Ar(m)",
+                   22: "E + Ar(r) => E + Ar(4p)"}
+
+    # 4) Set values in params class
+    params.D[0]    = De
+    params.D[1]    = Di
+    params.D[2]    = Dm
+    params.D[3]    = Dr
+    params.D[4]    = D4p
+
+    params.mu[0]   = mue
+    params.mu[1]   = mui
+    params.mu[2]   = mum
+    params.mu[3]   = mur
+    params.mu[4]   = mu4p
+
+    params.A[:]    = Ck[:]
+    params.B[:]    = B[:]
+    params.C[:]    = A[:]
+
+    # Account for the 2/3 term to convert from electron temperature to electron energy
+    for i in range(len(params.A)):
+        params.A[i] *= (2/3)**(params.B[i])
+
+    params.dH[:]   = dH[:]
+    params.dEps[:] = dEps[:]
+    params.qStar   = qStar
+    params.alpha   = alpha
+    params.ks      = ks
+    params.gam     = gam
+    params.kappaB  = kappaB
+    params.nAronp0 = nAr / np0
+    params.p0      = p0
+    params.Tg0     = Tg0
+    params.EC      = 2.0 * me / mAr \
+        * np.sqrt(16.0 * (me + mAr) * e0 * c**2
+                  / (3.0 * np.pi * me * mAr)) * se * nAr * tau
+    # params.EC = 2.0 * me / mAr * 3.8e9 * tau
+
+    params.verticalShift = verticalShift / V0
+
+    # Parameters needed to compute the current with dimensions
+    params.V0Ltau  = V0 / (L * tau)
+    params.V0L     = V0 / L
+    params.LLV0tau = (L*L) / (V0*tau)
+    params.tauL    = L / tau
+    params.np0     = np0           # "nominal" electron density [1/m^3]
+    params.qe      = qe            # unit charge [C]
+    params.eps0    = eps0          # unit charge [C]
+    params.eArea   = electrodeArea # electrode area [m^2]
+
+
+    reactionExpressionslist = [f"{params.A[0]} * energy**{params.B[0]} * np.exp(-{params.C[0]} / energy)",
+                               f"{params.A[1]} * energy**{params.B[1]} * np.exp(-{params.C[1]} / energy)",
+                               f"{params.A[2]} * energy**{params.B[2]} * np.exp(-{params.C[2]} / energy)",
+                               f"{params.A[3]} * energy**{params.B[3]} * np.exp(-{params.C[3]} / energy)",
+                               f"{params.A[4]} * energy**{params.B[4]} * np.exp(-{params.C[4]} / energy)",
+                               f"{params.A[5]} * energy**{params.B[5]} * np.exp(-{params.C[5]} / energy)",
+                               f"{params.A[6]} * energy**{params.B[6]} * np.exp(-{params.C[6]} / energy)",
+                               f"{params.A[7]} * energy**{params.B[7]} * np.exp(-{params.C[7]} / energy)",
+                               f"{params.A[8]} * energy**{params.B[8]} * np.exp(-{params.C[8]} / energy)",
+                               f"{params.A[9]} * energy**{params.B[9]} * np.exp(-{params.C[9]} / energy)",
+                               f"{params.A[10]} * energy**{params.B[10]} * np.exp(-{params.C[10]} / energy)",
+                               f"{params.A[11]} * energy**{params.B[11]} * np.exp(-{params.C[11]} / energy)",
+                               f"{params.A[12]} * energy**{params.B[12]} * np.exp(-{params.C[12]} / energy)",
+                               f"{params.A[13]} * energy**{params.B[13]} * np.exp(-{params.C[13]} / energy)",
+                               f"{params.A[14]} * energy**{params.B[14]} * np.exp(-{params.C[14]} / energy)",
+                               f"{params.A[15]} * energy**{params.B[15]} * np.exp(-{params.C[15]} / energy)",
+                               f"{params.A[16]} * energy**{params.B[16]} * np.exp(-{params.C[16]} / energy)",
+                               f"{params.A[17]} * energy**{params.B[17]} * np.exp(-{params.C[17]} / energy)",
+                               f"{params.A[18]} * energy**{params.B[18]} * np.exp(-{params.C[18]} / energy)",
+                               f"{params.A[19]} * energy**{params.B[19]} * np.exp(-{params.C[19]} / energy)",
+                               f"{params.A[20]} * energy**{params.B[20]} * np.exp(-{params.C[20]} / energy)",
+                               f"{params.A[21]} * energy**{params.B[21]} * np.exp(-{params.C[21]} / energy)",
+                               f"{params.A[22]} * energy**{params.B[22]} * np.exp(-{params.C[22]} / energy)"]
+
+    reactionTExpressionslist = [f"{params.A[0]} * (energy**({params.B[0]}-1)) * np.exp(-{params.C[0]}/energy) * ({params.B[0]} + {params.C[0]}/energy)",
+                                f"{params.A[1]} * (energy**({params.B[1]}-1)) * np.exp(-{params.C[1]}/energy) * ({params.B[1]} + {params.C[1]}/energy)",
+                                f"{params.A[2]} * (energy**({params.B[2]}-1)) * np.exp(-{params.C[2]}/energy) * ({params.B[2]} + {params.C[2]}/energy)",
+                                f"{params.A[3]} * (energy**({params.B[3]}-1)) * np.exp(-{params.C[3]}/energy) * ({params.B[3]} + {params.C[3]}/energy)",
+                                f"{params.A[4]} * (energy**({params.B[4]}-1)) * np.exp(-{params.C[4]}/energy) * ({params.B[4]} + {params.C[4]}/energy)",
+                                f"{params.A[5]} * (energy**({params.B[5]}-1)) * np.exp(-{params.C[5]}/energy) * ({params.B[5]} + {params.C[5]}/energy)",
+                                f"{params.A[6]} * (energy**({params.B[6]}-1)) * np.exp(-{params.C[6]}/energy) * ({params.B[6]} + {params.C[6]}/energy)",
+				f"{params.A[7]} * (energy**({params.B[7]}-1)) * np.exp(-{params.C[7]}/energy) * ({params.B[7]} + {params.C[7]}/energy)",
+		                f"{params.A[8]} * (energy**({params.B[8]}-1)) * np.exp(-{params.C[8]}/energy) * ({params.B[8]} + {params.C[8]}/energy)",
+                                f"{params.A[9]} * (energy**({params.B[9]}-1)) * np.exp(-{params.C[9]}/energy) * ({params.B[9]} + {params.C[9]}/energy)",
+                                f"{params.A[10]} * (energy**({params.B[10]}-1)) * np.exp(-{params.C[10]}/energy) * ({params.B[10]} + {params.C[10]}/energy)",
+                                f"{params.A[11]} * (energy**({params.B[11]}-1)) * np.exp(-{params.C[11]}/energy) * ({params.B[11]} + {params.C[11]}/energy)",
+                                f"{params.A[12]} * (energy**({params.B[12]}-1)) * np.exp(-{params.C[12]}/energy) * ({params.B[12]} + {params.C[12]}/energy)",
+                                f"{params.A[13]} * (energy**({params.B[13]}-1)) * np.exp(-{params.C[13]}/energy) * ({params.B[13]} + {params.C[13]}/energy)",
+                                f"{params.A[14]} * (energy**({params.B[14]}-1)) * np.exp(-{params.C[14]}/energy) * ({params.B[14]} + {params.C[14]}/energy)",
+                                f"{params.A[15]} * (energy**({params.B[15]}-1)) * np.exp(-{params.C[15]}/energy) * ({params.B[15]} + {params.C[15]}/energy)",
+                                f"{params.A[16]} * (energy**({params.B[16]}-1)) * np.exp(-{params.C[16]}/energy) * ({params.B[16]} + {params.C[16]}/energy)",
+                                f"{params.A[17]} * (energy**({params.B[17]}-1)) * np.exp(-{params.C[17]}/energy) * ({params.B[17]} + {params.C[17]}/energy)",
+                                f"{params.A[18]} * (energy**({params.B[18]}-1)) * np.exp(-{params.C[18]}/energy) * ({params.B[18]} + {params.C[18]}/energy)",
+                                f"{params.A[19]} * (energy**({params.B[19]}-1)) * np.exp(-{params.C[19]}/energy) * ({params.B[19]} + {params.C[19]}/energy)",
+                                f"{params.A[20]} * (energy**({params.B[20]}-1)) * np.exp(-{params.C[20]}/energy) * ({params.B[20]} + {params.C[20]}/energy)",
+                                f"{params.A[21]} * (energy**({params.B[21]}-1)) * np.exp(-{params.C[21]}/energy) * ({params.B[21]} + {params.C[21]}/energy)",
+                                f"{params.A[22]} * (energy**({params.B[22]}-1)) * np.exp(-{params.C[22]}/energy) * ({params.B[22]} + {params.C[22]}/energy)"]
+           
+
+    reactionExpressionTypelist =  np.array([False,False,False,False,False,False,False,False,False,
+                                           True,True,True,True,False,True,True,True,False,True,True,False,True,True])
+
+    reactionsList = []
+    LOGFilename = 'interpolationSample.log'
+    f = open(LOGFilename, 'w')
+
+    for i in range(Nr):
+        if reactionExpressionTypelist[i]:
+            if (i < 15):
+                f = h5.File("../BOLSIGChemistry_6SpeciesRates/{0:s}.h5".format(rxnNameDict[i]), 'r')
+                dataset = f["table"]
+            else:
+                f = h5.File("../BOLSIGChemistry_6SpeciesRates/StepwiseExcitations.nominal.h5", 'r')
+                dataset = f[rxnNameDict[i]]
+
+            Te = dataset[:,0]
+            Te /= 11604
+            rateCoeff = dataset[:,1]
+            rateCoeff /= 6.022e23
+
+            # Sorting mean energy array and rate coefficient array based on
+            # the mean energy array.
+            Teinds = Te.argsort()
+            rateCoeff = rateCoeff[Teinds]
+            Te = Te[Teinds]
+
+            # Find duplicates
+            TeDuplicateinds = np.where(np.abs(np.diff(Te, axis=0)) > 0.0)
+            TeDuplicateindsForLog = np.where(np.abs(np.diff(Te, axis=0)) == 0.0)
+            rateCoeff = rateCoeff[TeDuplicateinds]
+            Te = Te[TeDuplicateinds]
+
+            # Nondimensionalization of mean energy.
+            Te *= 1.5
+
+            # Find first non-zero value of the coefficient rate.
+            I = np.nonzero(rateCoeff)
+
+            diffRateCoeff = [j-i for i, j in zip(rateCoeff[:-1], rateCoeff[1:])]
+            diffTe = [j-i for i, j in zip(Te[:-1], Te[1:])]
+
+            Monotonicity = np.asarray([j/i for i, j in zip(diffTe, diffRateCoeff)])
+            Monotonicity = np.insert(Monotonicity, 0, 0.0, axis=0)
+
+            Nan = np.isnan(Monotonicity)
+            Inf = np.isinf(Monotonicity)
+            indexPositive = np.where(Monotonicity>0.0)
+            Positive = np.full(Monotonicity.shape, False, dtype=bool)
+            Positive[indexPositive] = True
+            
+            indices = Nan + Inf + Positive
+
+
+            #lastFalse = np.where(indices==False)[-1][-1] + 2
+            for k in range(len(Te)):
+               if Te[k] < 4.5 and indices[k] == False:
+                  lastFalse = k + 2
+
+            # Transformation to log scale.
+            TeLog = np.log(Te)
+
+            # Compute the slope of the rate coefficient between its first two non-zero values.
+            # Finite differences are used.
+            dydx = (rateCoeff[lastFalse + 1] - rateCoeff[lastFalse]) \
+                 / (Te[lastFalse + 1] - Te[lastFalse])
+
+            # Arrhenius form: kf = A * exp(-C / Te)
+            C = Te[lastFalse]**2.0*dydx / rateCoeff[lastFalse]
+
+            # Compute pre-exponential coefficient, A, in log scale.
+            ALog = np.log(rateCoeff[lastFalse]) + C / Te[lastFalse]
+
+            # Transform rate coefficient in log scale.
+            rateCoeffLog = np.zeros(rateCoeff.shape)
+            rateCoeffLog[lastFalse:] = np.log(rateCoeff[lastFalse:])
+            # For the troublesome values, we use the Arrhenius form.
+            rateCoeffLog[0:lastFalse] = ALog - C / Te[0:lastFalse]
+            # Nondimensionalization in log scale.
+            if (i < 13):
+                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
+            else:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(np0)
+
+            # Interpolation in log scale.
+            reactionExpressionsLog = CubicSpline(TeLog, rateCoeffLog)
+            # Gradient in log scale
+            reactionTExpressionsLog = CubicSpline.derivative(reactionExpressionsLog)
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf_log = reactionExpressionsLog,
+                                kf_T_log = reactionTExpressionsLog)
+            reactionsList.append(reaction)
+
+            logging.basicConfig(filename=LOGFilename)
+            logging.warning('Interpolation info (Reaction %s):', i + 1)
+            logging.warning('First non-zero entry in the rate coefficient: %s', I[0][0])
+            logging.warning('Monotonicity of the rate coefficient start from entry: %s', lastFalse)
+            logging.warning('Position of possible duplicates in mean energy array: %s',
+                          TeDuplicateindsForLog[0])
+
+        else:
+            rxn   = eval("lambda energy :" + reactionExpressionslist[i])
+            rxn_T = eval("lambda energy :" + reactionTExpressionslist[i])
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf = rxn, kf_T = rxn_T)
+            reactionsList.append(reaction)
+
+    params.reactionsList = reactionsList
+
+    diffList = []
+    # Data from BOLSIG
+    # Te in [eV]
+    # De * N in [1/(m*s)]
+    #NDe_v_Te = np.array([ [-0.05, 1.8e25],         [0.0, 1.8e25],           [0.05, 1.8e25],          [0.1, 1.8e25],
+    #                      [0.2103718, 1.8e25],     [0.2244455, 1.8e25],     [0.2390528, 1.8e25],     [0.2544605, 1.8e25],
+    #                      [0.2710021, 1.8e25],     [0.2886776, 1.8e25],     [0.3078205, 1.8e25],     [0.3286309, 1.8e25],
+    #                      [0.3513089, 1.8e25],     [0.3760546, 1.8e25],     [0.4032682, 1.8e25],     [0.4331498, 1.8e25],
+    #                      [0.4659662, 1.8e25],     [0.5021843, 1.8e25],     [0.5424044, 1.8e25],     [0.5868266, 1.8e25],
+    #                      [0.6359845, 1.8e25],     [0.690345, 1.8e25],      [0.749041, 1.8e25],      [0.813073, 1.8e25],
+    #                      [1.217942, 1.46E+25],    [1.319326, 1.39E+25],    [1.430048, 1.32E+25],    [1.550108, 1.26E+25],
+    #                      [1.681507, 1.20E+25],    [1.823578, 1.15E+25],    [1.977655, 1.09E+25],    [2.143071, 1.05E+25],
+    #                      [2.319826, 9.98E+24],    [2.508587, 9.54E+24],    [2.709354, 9.13E+24],    [2.916124, 8.75E+24],
+    #                      [3.112889, 8.45E+24],    [3.272969, 8.24E+24],    [3.383691, 8.14E+24],    [3.456394, 8.09E+24],
+    #                      [3.505085, 8.07E+24],    [3.544438, 8.05E+24],    [3.579789, 8.03E+24],    [3.616474, 8.01E+24],
+    #                      [3.656494, 7.97E+24],    [3.701183, 7.93E+24],    [3.751875, 7.87E+24],    [3.807903, 7.81E+24],
+    #                      [3.871268, 7.75E+24],    [3.941303, 7.68E+24],    [4.018675, 7.61E+24],    [4.102717, 7.53E+24],
+    #                      [4.193429, 7.5E+24],     [4.290811, 7.5E+24],     [4.39553, 7.5E+24],      [4.507586, 7.5E+24],
+    #                      [5., 7.5E+24],           [6., 7.5E+24],           [7., 7.5E+24],           [8., 7.5E+24],
+    #                      [9., 7.5E+24],           [10., 7.5E+24],          [11., 7.5E+24],          [12., 7.5E+24]])
+
+    transport = h5.File("../BOLSIGChemistry_Transport/nominal_transport.h5", 'r')
+    NDe_v_Te = transport["diffusivity"]
+    Te_trans = NDe_v_Te[:,0]
+    Te_trans /= 11604
+    print("Te_min = {0:.6e}".format(NDe_v_Te[0,0]))
+    print("Te_max = {0:.6e}".format(NDe_v_Te[-1,0]))
+    De_interp = (NDe_v_Te[:,1]/nAr)*tau/(L*L)
+    De_spline = CubicSpline(Te_trans, De_interp)
+    De_Te_spline = CubicSpline.derivative(De_spline)
+    diffusivity = Diffusivity(interpolate = True, D_expression = De_spline, D_T_expression = De_Te_spline)
+    diffList.append(diffusivity)
+
+    Ns = 6
+    for i in range(1, Ns):
+        diffList.append(Diffusivity(interpolate = False))
+
+    params.diffusivityList = diffList
+
+    muList = []
+    # Data from BOLSIG
+    # Te in [eV]
+    # Mue * N in [1/(V*m*s)]
+    #Nmue_v_Te = np.array([[0.02846756, 1.289e+26], [0.02975487, 1.33e+26],   [0.03173586, 1.383e+26],  [0.03477738,1.448e+26],
+    #                      [0.03937968, 1.523e+26], [0.04614973, 1.604e+26],  [0.05561446, 1.679e+26],  [0.0681674,1.735e+26],
+    #                      [0.0835084, 1.749e+26],  [0.1008504, 1.718e+26],   [0.1187927, 1.639e+26],   [0.1363348,1.522e+26],
+    #                      [0.1528764, 1.386e+26],  [0.1682841, 1.245e+26],   [0.1826913, 1.108e+26],   [0.1965649,9.809e+25],
+    #                      [0.2103718, 8.662e+25],  [0.2244455, 7.641e+25],   [0.2390528, 6.74e+25],    [0.2544605,5.947e+25],
+    #                      [0.2710021, 5.249e+25],  [0.2886776, 4.636e+25],   [0.3078205, 4.097e+25],   [0.3286309,3.623e+25],
+    #                      [0.3513089, 3.206e+25],  [0.3760546, 2.839e+25],   [0.4032682, 2.517e+25],   [0.4331498,2.232e+25],
+    #                      [0.4659662, 1.981e+25],  [0.5021843, 1.76e+25],    [0.5424044, 1.565e+25],   [0.5868266,1.392e+25],
+    #                      [0.6359845, 1.239e+25],  [0.690345, 1.103e+25],    [0.749041,  9.8e+24],     [0.813073,8.699e+24],
+    #                      [0.882441, 7.711e+24],   [0.957145, 6.828e+24],    [1.037185,  6.04e+24],    [1.123895,5.344e+24],
+    #                      [1.217942, 4.729e+24],   [1.319326, 4.186e+24],    [1.430048,  3.707e+24],   [1.550108,3.283e+24],
+    #                      [1.681507, 2.907e+24],   [1.823578, 2.574e+24],    [1.977655,  2.278e+24],   [2.143071,2.015e+24],
+    #                      [2.319826, 1.779e+24],   [2.508587, 1.569e+24],    [2.709354,  1.384e+24],   [2.916124,1.228e+24],
+    #                      [3.112889, 1.115e+24],   [3.272969, 1.055e+24],    [3.383691,  1.038e+24],   [3.456394,1.044e+24],
+    #                      [3.505085, 1.054e+24],   [3.544438, 1.062e+24],    [3.579789,  1.064e+24],   [3.616474, 1.059e+24],
+    #                      [3.656494, 1.048e+24],   [3.701183, 1.033e+24],    [3.751875,  1.014e+24],   [3.807903, 9.928e+23],
+    #                      [3.871268, 9.697e+23],   [3.941303,  9.459e+23],   [4.018675, 9.22e+23],     [4.102717, 8.991e+23],
+    #                      [4.193429, 8.773e+23],   [4.290811,  8.57e+23],    [4.39553, 8.383e+23],     [4.507586, 8.21e+23],
+    #                      [4.627646, 8.051e+23],   [4.757711,  7.901e+23],   [4.899115, 7.757e+23],    [5.054526, 7.617e+23],
+    #                      [5.227946, 7.479e+23],   [5.423377,  7.339e+23],   [5.646822, 7.198e+23],    [5.905618, 7.056e+23],
+    #                      [6.20977, 6.91e+23],     [6.571284,  6.757e+23],   [7.01017, 6.607e+23],     [7.54377, 6.458e+23],
+    #                      [8.19743, 6.303e+23],    [9.01117,   6.155e+23],   [10.02501, 6e+23],        [11.30565, 5.843e+23],
+    #                      [12.89978, 5.677e+23],   [14.91412,  5.51e+23],    [17.41537, 5.326e+23],    [20.57028, 5.149e+23],
+    #                      [24.51225, 4.968e+23],   [29.45472,  4.784e+23],   [35.6845, 4.602e+23],     [43.58845, 4.421e+23],
+    #                      [53.86692, 4.269e+23],   [67.367,    4.134e+23],   [85.4427, 4.026e+23],     [100.0, 4.026e+23]])
+
+    Nmue_v_Te = transport["mobility"]
+    #Te = Nmue_v_Te[:,0]
+    #Te /= 11604
+    mue_interp = (Nmue_v_Te[:,1]/nAr)*V0*tau/(L*L)
+    mue_spline = CubicSpline(Te_trans, mue_interp)
+    mue_Te_spline = CubicSpline.derivative(mue_spline)
+    mobility = Mobility(interpolate = True, mu_expression = mue_spline, mu_T_expression = mue_Te_spline)
+    muList.append(mobility)
+
+    Ns = 6
+    for i in range(1, Ns):
+        muList.append(Mobility(interpolate = False))
+
+    params.mobilityList = muList
+    
+    # 5) Dump to screen
+    params.print()
diff --git a/psaapProperties_6Species_Sampling.py b/psaapProperties_6Species_Sampling.py
index 9abed73e7..79cdd27e6 100755
--- a/psaapProperties_6Species_Sampling.py
+++ b/psaapProperties_6Species_Sampling.py
@@ -14,6 +14,23 @@ def __init__(self, *initial_data, **kwargs):
         for key in kwargs:
             setattr(self, key, kwargs[key])
 
+class Diffusivity(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Mobility(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+
 def setPsaapProperties_6Species_Sampling(gam, inputV0, inputVDC, params, Nr, iSample):
     """Sets non-dimensional properties corresponding to Liu 2014 paper.
 
@@ -62,25 +79,20 @@ def setPsaapProperties_6Species_Sampling(gam, inputV0, inputVDC, params, Nr, iSa
 
     # transport parameters
     nmue = 9.66e21   # argon number density times electron mobility [1/(V*cm*s)]
-    #nmui = 4.65e19   # argon number density times ion mobility [1/(V*cm*s)]
-    #nmum = 1 / (np.sqrt(16.0 * (mAr + mAr) * 300 * 8.62e-5 * c**2
-    #                    / (3.0 * np.pi * mAr * mAr)) * se * mAr * 1.6e-19 / c**2)
     nmum = 0.0
     nmui = 8.0e19
-    #nmum = 9.35e19
     nDe  = 3.86e22   # argon number density times electron diffusivity [1/(cm*s)]
     nDi  = 2.07e18   # argon number density times ion diffusivity [1/(cm*s)]
     nDm  = 2.42e18   # argon number density times metastable diffusivity [1/(cm*s)]
-    #nDm  = 3.914e20
 
     # reaction parameters (NB: k_i = Ck*Ee^B*exp(-A/Ee))
     #                          Ee = 3/2*Te (Te in eV)
     #                          -> k_i = [Ck*(2/3)^B] * Ee^B * exp[-(3/2)*A/Ee]
     # nominal
-    Ck = np.array([2.0e-7,2.1e-9,5.0e-10,6.4e-10,2.1e-15,1.0e-5,3.2e7,3.0e7,3.0e7,0.0,0.0,0.0,0.0,4.3e-10,0.0,3.7e-8,8.9e-7,1.8e-7,3.0e-7,3.0e-7,4.3e-10,9.1e-7,8.9e-7]) # pre-exponential factors [cm^3/s]
+    Ck = np.array([2.0e-7,2.1e-9,5.0e-10,6.4e-10,2.1e-15,1.32e8,3.2e7,3.0e7,3.0e7,0.0,0.0,0.0,0.0,4.3e-10,0.0,3.7e-8,8.9e-7,1.8e-7,3.0e-7,3.0e-7,4.3e-10,9.1e-7,8.9e-7]) # pre-exponential factors [cm^3/s]
     B  = np.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.74,0.0,0.0,0.51,0.61,0.51,0.51,0.74,0.0,0.51])
     A  = np.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.59,2.61,0.0,0.0,0.0,0.0,1.59]) # activation temperature [eV]
-    dH = np.array([0.0,-7.412,-10.054,-7.336,0.0,0.0,0.0,0.0,0.0,11.548,11.624,12.907,15.76,-11.548,4.212,0.076,1.359,2.853,-1.283,-1.359,-11.624,-0.076,0.983]) # energy lost per electron due to ionization rxn [eV]
+    dH = np.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.548,11.624,12.907,15.76,-11.548,4.212,0.076,1.359,2.853,-1.283,-1.359,-11.624,-0.076,0.983]) # energy lost per electron due to ionization rxn [eV]
     dEps = np.array([0.0,15.76,11.548,11.624,12.907,0.0])
 
     # BC parameters
@@ -109,7 +121,7 @@ def setPsaapProperties_6Species_Sampling(gam, inputV0, inputVDC, params, Nr, iSa
     nmui *= 100. # 1/(V*m*s)
     Ck[0:5]  *= 1e-6 # m^3/s
     Ck[5:9]  *= 1 # 1/s
-    Ck[9:22] *= 1e-6 # m^6/s
+    Ck[9:]   *= 1e-6 # m^6/s
     ks   *= 0.01 # m/s
     se   *= 1.0e-20  # m^2
 
@@ -183,29 +195,31 @@ def setPsaapProperties_6Species_Sampling(gam, inputV0, inputVDC, params, Nr, iSa
             # Rxn22: E + AR(r)      ->   E + AR(m)
             # Rxn23: E + AR(r)      ->   E + AR(4p)
 
-    rxnNameDict = {0: "2AR(m) => 2AR",
-                   1: "AR(m) + AR(r) => E + AR+ + AR",
-                   2: "2AR(4p) => E + AR+ + AR",
-                   3: "2AR(m) => E + AR+ + AR",
-                   4: "AR(m) + AR => 2AR",
-                   5: "AR(r) => AR",
-                   6: "AR(4p) => AR",
-                   7: "AR(4p) => AR(m)",
-                   8: "Ar(4p) => AR(r)",
+    rxnNameDict = {0: "Ar(m) + Ar(m) => Ar + Ar",
+                   1: "Ar(m) + Ar(r) => E + Ar + Ar+",
+                   2: "Ar(4p) + Ar(4p) => E + Ar + Ar+",
+                   3: "Ar(m) + Ar(m) => E + Ar + Ar+",
+                   4: "Ar + Ar(m) => Ar + Ar",
+                   5: "Ar(r) => Ar",
+                   6: "Ar(4p) => Ar",
+                   7: "Ar(4p) => Ar(m)",
+                   8: "Ar(4p) => Ar(r)",
                    9: "1s-metastable",
                    10: "1s-resonance",
                    11: "2p-lumped",
                    12: "Ionization",
-                   13: "E + AR(m) => E + AR",
+                   13: "E + Ar(m) => E + Ar",
+                   #13: "deexci-metastable",
                    14: "StepIonization",
-                   15: "E + AR(m) => E + AR(r)",
-                   16: "E + AR(m) => E + AR(4p)",
-                   17: "E + AR(4p) => 2E + AR+",
-                   18: "E + AR(4p) => E + AR(r)",
-                   19: "E + AR(4p) => E + AR(m)",
-                   20: "E + AR(r) => E + AR",
-                   21: "E + AR(r) => E + AR(m)",
-                   22: "E + AR(r) => E + AR(4p)"}
+                   15: "E + Ar(m) => E + Ar(r)",
+                   16: "E + Ar(m) => E + Ar(4p)",
+                   17: "E + Ar(4p) => E + E + Ar+",
+                   18: "E + Ar(4p) => E + Ar(r)",
+                   19: "E + Ar(4p) => E + Ar(m)",
+                   20: "E + Ar(r) => E + Ar",
+                   #20: "deexci-resonance",
+                   21: "E + Ar(r) => E + Ar(m)",
+                   22: "E + Ar(r) => E + Ar(4p)"}
 
     # 4) Set values in params class
     params.D[0]    = De
@@ -252,36 +266,31 @@ def setPsaapProperties_6Species_Sampling(gam, inputV0, inputVDC, params, Nr, iSa
     params.eArea   = electrodeArea # electrode area [m^2]
 
 
-    reactionExpressionTypelist = np.array([False,False,False,False,False,False,False,False,False,
-                                           True,True,True,True,False,True,False,False,False,False,False,False,False,False])
+    reactionExpressionTypelist =  np.array([False,False,False,False,False,False,False,False,False,
+                                           True,True,True,True,False,True,True,True,False,True,True,False,True,True])
 
     reactionsList = []
     LOGFilename = 'interpolationSample%s.log'%str(iSample)
     f = open(LOGFilename, 'w')
 
     for i in range(Nr):
-        Nsample = 20
-        N300 = 200
-        sample_root_dir = "../6species_SampleFiles"
+        sample_root_dir = "../BOLSIGChemistry_6SpeciesRates_Original"
         if reactionExpressionTypelist[i]:
-            #Nsample = 1
-            #N300 = 200
-            fileString = sample_root_dir + "/" + rxnNameDict[i]
-            fileName = "%s.%08d.h5" % (fileString, iSample)
-            f = h5.File(fileName, 'r')
-            dataset = f["table"]
+            if i < 15 or i == 20:
+                fileString = sample_root_dir + "/" + rxnNameDict[i]
+                fileName = "%s.%08d.h5" % (fileString, iSample)
+                f = h5.File(fileName, 'r')
+                dataset = f["table"]
+            else:
+                fileString = sample_root_dir + "/" + "StepwiseExcitations"
+                fileName = "%s.%08d.h5" % (fileString, iSample)
+                f = h5.File(fileName, 'r')
+                dataset = f[rxnNameDict[i]]
             
-            #rateCoeff = np.fromfile(rate_file)
             rateCoeff = dataset[:,1]
             rateCoeff /= 6.022e23
-            #rateCoeff = np.reshape(rateCoeff,[Nsample, N300]).T[:,iSample]
-            #rateCoeff = np.reshape(rateCoeff,[1, N300]).T[:,1]
-
-            #Te = np.fromfile(temp_file)
             Te = dataset[:,0]
             Te /= 11604.
-            #Te = np.reshape(Te,[Nsample, N300]).T[:,iSample]
-            #Te = np.reshape(Te,[1, N300]).T[:,1]
 
             # Sorting mean energy array and rate coefficient array based on
             # the mean energy array.
@@ -318,7 +327,7 @@ def setPsaapProperties_6Species_Sampling(gam, inputV0, inputVDC, params, Nr, iSa
             #lastFalse = np.where(indices==False)[-1][-1] + 2
             for k in range(len(Te)):
                 if (Te[k] < 4.5 and indices[k] == False):
-                    lasFalse = k + 2
+                    lastFalse = k + 2
 
             # Transformation to log scale.
             TeLog = np.log(Te)
@@ -384,22 +393,19 @@ def setPsaapProperties_6Species_Sampling(gam, inputV0, inputVDC, params, Nr, iSa
                 A *= tau*nAr
             elif (i > 4 and i < 9):
                 A *= tau
-            elif (i > 8 and i < 13):
-                A /= 6.022e23
-                A *= tau*nAr
+            #elif (i > 8 and i < 13):
+            #    A /= 6.022e23
+            #    A *= tau*nAr
             else:
                 A /= 6.022e23
                 A *= tau*np0
 
-            A *= 11604**B 
-            C = C*1.5/e0
+            A *= ((2./3.)*11604)**B 
+            C *= 1.5/(e0*11604)
 
             rxn = eval("lambda energy :" + f"{A} * energy**{B} * np.exp(-{C} / energy)")
             rxn_T = eval("lambda energy :" + f"{A} * energy**({B}-1) * np.exp(-{C} / energy) * ({B} + {C} / energy)")
 
-            #rxn   = eval("lambda energy :" + reactionExpressionslist[i])
-            #rxn_T = eval("lambda energy :" + reactionTExpressionslist[i])
-
             reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
                                 rxnBolsig = reactionExpressionTypelist[i],
                                 kf = rxn, kf_T = rxn_T)
@@ -407,5 +413,45 @@ def setPsaapProperties_6Species_Sampling(gam, inputV0, inputVDC, params, Nr, iSa
 
     params.reactionsList = reactionsList
 
+    diffList = []
+    # Data from BOLSIG
+    # Te in [eV]
+    # De * N in [1/(m*s)]
+    transport = h5.File("../BOLSIGChemistry_6SpeciesRates_Original/Transport.%08d.h5" % (iSample), 'r')
+    #Te = NDe_v_Te[:,0]
+    NDe_v_Te = transport["diffusivity"]
+    Te_trans = NDe_v_Te[:,0]
+    Te_trans /= 11604
+    print("Te_min = {0:.6e}".format(NDe_v_Te[0,0]))
+    print("Te_max = {0:.6e}".format(NDe_v_Te[-1,0]))
+    De_interp = (NDe_v_Te[:,1]/nAr)*tau/(L*L)
+    De_spline = CubicSpline(Te_trans, De_interp)
+    De_Te_spline = CubicSpline.derivative(De_spline)
+    diffusivity = Diffusivity(interpolate = True, D_expression = De_spline, D_T_expression = De_Te_spline)
+    diffList.append(diffusivity)
+
+    Ns = 6
+    for i in range(1, Ns):
+        diffList.append(Diffusivity(interpolate = False))
+
+    params.diffusivityList = diffList
+
+    muList = []
+    # Data from BOLSIG
+    # Te in [eV]
+    # Mue * N in [1/(V*m*s)]
+    Nmue_v_Te = transport["mobility"]
+    mue_interp = (Nmue_v_Te[:,1]/nAr)*V0*tau/(L*L)
+    mue_spline = CubicSpline(Te_trans, mue_interp)
+    mue_Te_spline = CubicSpline.derivative(mue_spline)
+    mobility = Mobility(interpolate = True, mu_expression = mue_spline, mu_T_expression = mue_Te_spline)
+    muList.append(mobility)
+
+    Ns = 6
+    for i in range(1, Ns):
+        muList.append(Mobility(interpolate = False))
+
+    params.mobilityList = muList
+
     # 5) Dump to screen
     params.print()
diff --git a/psaapProperties_6Species_Sampling_100mTorr_Expanded.py b/psaapProperties_6Species_Sampling_100mTorr_Expanded.py
new file mode 100755
index 000000000..4824dfcd5
--- /dev/null
+++ b/psaapProperties_6Species_Sampling_100mTorr_Expanded.py
@@ -0,0 +1,494 @@
+import numpy as np
+from scipy.interpolate import CubicSpline
+import csv
+import matplotlib.pyplot as plt
+import matplotlib.colors as mcolors
+import h5py as h5
+import logging
+
+class Reaction(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Diffusivity(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Mobility(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+
+def setPsaapProperties_6Species_Sampling_100mTorr_Expanded(gam, inputV0, inputVDC, params, Nr, iSample):
+    """Sets non-dimensional properties corresponding to Liu 2014 paper.
+
+    Inputs:
+      gam : Secondary electron emission coefficient
+      params : chebSolver.modelParams class
+
+    Outputs: None
+      params data is overwritten using values from Liu 2014.
+    """
+    ###################################################################
+    # User specified parameters (you may change these if you wish to
+    # run a different scenario from Liu 2014)
+    ###################################################################
+
+    # densities
+    nAr = 3.22e21     # background number density of Ar [1/m^3] (corresponds to p = 100 mTorr)
+    np0 = 8e16        # "nominal" electron density [1/m^3]
+
+    # masses
+    # me = 9.10938356e-31        # mass of an electron [kg]
+    # me = 5.489e-4              # mass of an electron [u]
+    me = 0.511e6                 # mass of an electron [eV/c2]
+    # mAr = 39.948               # mass of an argon atom [u]
+    # mAr = 39.948 * 1.66054e-27 # mass of an argon atom [kg]
+    mAr = 37.2158e9              # mass of an electron [eV/c2]
+    # u = 931.4941e6             # eV/c2
+    c = 299792458                # speed of light [m/s]
+    se = 40                      # momentum cross section [A^2]
+
+    # nominal electron energy
+    e0 = 1.0  # [eV]
+
+    # pressure
+    p  = 13.3*1.5      # [J/m^3] *1.5 to convert it to energy (100 mTorr)
+
+    # gas energy at the wall
+    Tg0 = 0.038778    # 3/2*300K*kB ~ (p0 - nT[:,0])/ntot
+
+    # characteristics of driving voltage
+    V0  = inputV0                 # amplitude of driving voltage [V]
+    verticalShift = inputVDC      # DC voltage (vertical shift in driving voltage)
+    tau = (1./13.56e6)             # period of driving voltage [s]
+    L   = 2.00*0.005              # half-gap-width [m] (gap width is 2 cm)
+    electrodeArea = np.pi*0.05**2 # electrode area [m^2] (electrode diameter = 0.1 m)
+
+    # Add voltage uncertainty
+    V0 += h5.File('../../../BOLSIGChemistry_Voltage/Voltage.%08d.h5' % (iSample), 'r')["V_Err"][0]
+
+    # transport parameters
+    nmue = 9.66e21   # argon number density times electron mobility [1/(V*cm*s)]
+    nmum = 0.0
+    nmui = 8.0e19
+    nDe  = 3.86e22   # argon number density times electron diffusivity [1/(cm*s)]
+    nDi  = 2.07e18   # argon number density times ion diffusivity [1/(cm*s)]
+    nDm  = 2.42e18   # argon number density times metastable diffusivity [1/(cm*s)]
+
+    # reaction parameters (NB: k_i = Ck*Ee^B*exp(-A/Ee))
+    #                          Ee = 3/2*Te (Te in eV)
+    #                          -> k_i = [Ck*(2/3)^B] * Ee^B * exp[-(3/2)*A/Ee]
+    # nominal
+    Ck = np.array([2.0e-13,2.1e-15,5.0e-16,6.4e-16,2.1e-21,1.32e8,1.72e7,1.50e7,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0e-18,4.0e-19,0.0,0.0,0.0,0.0,2.5e-17,2.5e-17,1.0e-15,1.0e-15,0.0,0.0]) # pre-exponential factors [m^3/s]
+    B  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-0.5,0,0,0,0,0,0,0,0,0,0])
+    A  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]) # activation temperature [eV]
+    dH = np.array([0.0,-7.541,-10.577,-7.393,0.0,0.0,0.0,0.0,11.577,11.725,13.168,15.76,-11.577,4.183,0.148,1.592,2.592,-1.444,-1.592,-11.725,-0.148,1.444,0.0,0.0,-4.183,-4.035,-2.592,-15.76,0.0,0.0,-8.985,-9.133,4.035,-13.168]) # energy lost per electron due to ionization rxn [eV]
+    dEps = np.array([0.0,15.76,11.577,11.725,13.168,0.0])
+
+    # BC parameters
+    # ks = 1.19e7  # electron recombination rate [cm/s]
+    ks = 1.366109824889323e7 # electron recombination rate [cm/s/eV]
+
+    ###################################################################
+    # Constants of nature (probably shouldn't change unless you have
+    # root privileges on universe)
+    ###################################################################
+    qe   = 1.6e-19   # unit charge [C]
+    eps0 = 8.86e-12  # permittivity of free space [F/m]
+    kB   = 1.38e-23  # Boltzmann constant [J/K]
+    # kB   = 8.62e−5 # Boltzmann constant [eV/K]
+
+
+    ###################################################################
+    # Calculate non-dimensional parameters
+    ###################################################################
+
+    # 1) Convert input units to base SI (except eV)
+    nDe  *= 100. # 1/(m*s)
+    nDi  *= 100. # 1/(m*s)
+    nDm  *= 100.
+    nmue *= 100. # 1/(V*m*s)
+    nmui *= 100. # 1/(V*m*s)
+    ks   *= 0.01 # m/s
+    se   *= 1.0e-20  # m^2
+
+    # 2) Compute "raw" transport parameters
+    De  = nDe/nAr
+    Di  = nDi/nAr
+    Dm  = nDm/nAr
+
+    mue = nmue/nAr
+    mui = nmui/nAr
+    mum = nmum/nAr
+
+    # 3) Compute non-dimensional properties required by solver
+    De    = De*tau/(L*L)
+    Di    = Di*tau/(L*L)
+    Dm    = Dm*tau/(L*L)
+
+    mue   = mue*V0*tau/(L*L)
+    mui   = mui*V0*tau/(L*L)
+    mum   = mum*V0*tau/(L*L)
+
+    Ck[0:4]  *= tau*np0
+    Ck[4]    *= tau*nAr
+    Ck[5:8]  *= tau
+    Ck[8:12] *= tau*nAr
+    Ck[12:24]  *= tau*np0
+    Ck[24:28] *= tau*np0*np0
+    Ck[28:30] *= tau*nAr
+    Ck[30:] *= tau*np0
+    A       = A*1.5/e0  # 1.5 to convert from temperature to energy
+    dH      = dH/e0
+    qStar   = V0/e0 # qe*V0/e0, since e0 in eV, need qe*V0 in eV, which is just V0 in V
+    alpha   = qe*np0*L*L/(V0*eps0)
+    ks      = ks*tau/L
+    p0      = p/qe/np0
+    kappaB  = 4.878171165833662*1.6129 # non-dimensional thermal conductivity of background specie
+                                # (2/3)*tau/L**2*Kb/np0/kB,
+                                # where Kb is the thermal conductivity of background specie
+
+    params.beta = np.array([[0,1,1,1,0,0,0,0,1,1,1,2,1,2,1,1,2,1,1,1,1,1,0,0,1,1,1,1,0,0,1,1,2,1],                     # E
+                            [0,1,1,1,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0],                     # AR+
+                            [0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,1,0,0,0,0,0],                     # AR(m)
+                            [0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0],                     # AR(r)
+                            [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0],                     # AR(4p)
+                            [2,1,1,1,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1]], dtype=np.int64)    # AR
+
+    params.alfa = np.array([[0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,0,0,0,0,1,1],                     # E
+                            [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0],                     # AR+
+                            [2,1,0,2,1,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],                     # AR(m)
+                            [0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,1,0],                     # AR(r)
+                            [0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,0,1],                     # AR(4p)
+                            [0,0,0,0,1,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0]], dtype=np.int64)    # AR
+    
+    # Rxn1:  2AR(m)         ->   2AR
+    # Rxn2:  AR(m) + AR(r)  ->   E + AR+ + AR
+    # Rxn3:  2AR(4p)        ->   E + AR+ + AR
+    # Rxn4:  2AR(m)         ->   E + AR+ + AR
+    # Rxn5:  AR(m) + AR     ->   2AR
+    # Rxn6:  AR(r)          ->   AR + hv
+    # Rxn7:  AR(4p)         ->   AR(m) + hv
+    # Rxn8:  AR(4p)         ->   AR(r) + hv
+    # Rxn9:  E + AR         ->   E + AR(m)
+    # Rxn10: E + AR         ->   E + AR(r)
+    # Rxn11: E + AR         ->   E + AR(4p)
+    # Rxn12: E + AR         ->   2E + AR+
+    # Rxn13: E + AR(m)      ->   E + AR
+    # Rxn14: E + AR(m)      ->   2E + AR+
+    # Rxn15: E + AR(m)      ->   E + AR(r)
+    # Rxn16: E + AR(m)      ->   E + AR(4p)
+    # Rxn17: E + AR(4p)     ->   2E + AR+
+    # Rxn18: E + AR(4p)     ->   E + AR(r)
+    # Rxn19: E + AR(4p)     ->   E + AR(m)
+    # Rxn20: E + AR(r)      ->   E + AR
+    # Rxn21: E + AR(r)      ->   E + AR(m)
+    # Rxn22: E + AR(r)      ->   E + AR(4p)
+    # Rxn23: E + AR+        ->   AR(m) + hv
+    # Rxn24: E + AR+        ->   AR(4p) + hv
+    # Rxn25: 2E + AR+       ->   E + AR(m)
+    # Rxn26: 2E + AR+       ->   E + AR(r)
+    # Rxn27: 2E + AR+       ->   E + AR(4p)
+    # Rxn28: 2E + AR+       ->   E + AR
+    # Rxn29: AR + AR(4p)    ->   AR + AR(m)
+    # Rxn30: AR + AR(4p)    ->   AR + AR(r)
+    # Rxn31: AR(m) + AR(4p) ->   E + AR+ + AR
+    # Rxn32: AR(r) + AR(4p) ->   E + AR+ + AR
+    # Rxn33: E + AR(r)      ->   2E + AR+
+    # Rxn34: E + AR(4p)     ->   E + AR 
+
+    rxnNameDict = {0: "Ar(m) + Ar(m) => Ar + Ar",
+                   1: "Ar(m) + Ar(r) => E + Ar + Ar+",
+                   2: "Ar(4p) + Ar(4p) => E + Ar + Ar+",
+                   3: "Ar(m) + Ar(m) => E + Ar + Ar+",
+                   4: "Ar + Ar(m) => Ar + Ar",
+                   5: "Ar(r) => Ar",
+                   6: "Ar(4p) => Ar(m)",
+                   7: "Ar(4p) => Ar(r)",
+                   8: "Excitation_Metastable",
+                   9: "Excitation_Resonant",
+                   10: "Excitation_4p",
+                   11: "Ionization",
+                   12: "DeExcitation_Metastable",
+                   13: "StepIonization_Metastable",
+                   14: "E + Ar(m) => E + Ar(r)",
+                   15: "E + Ar(m) => E + Ar(4p)",
+                   16: "StepIonization_4p",
+                   17: "E + Ar(4p) => E + Ar(r)",
+                   18: "E + Ar(4p) => E + Ar(m)",
+                   19: "DeExcitation_Resonant",
+                   20: "E + Ar(r) => E + Ar(m)",
+                   21: "E + Ar(r) => E + Ar(4p)",
+                   22: "E + Ar+ => Ar(m)",
+                   23: "E + Ar+ => Ar(4p)",
+                   24: "3BdyRecomb_Metastable",
+                   25: "3BdyRecomb_Resonant",
+                   26: "3BdyRecomb_4p",
+                   27: "3BdyRecomb_Ground",
+                   28: "Ar + Ar(4p) => Ar + Ar(m)",
+                   29: "Ar + Ar(4p) => Ar + Ar(r)",
+                   30: "Ar(m) + Ar(4p) => E + Ar+ + Ar",
+                   31: "Ar(r) + Ar(4p) => E + Ar+ + Ar",
+                   32: "StepIonization_Resonant",
+                   33: "DeExcitation_4p"}
+
+    # 4) Set values in params class
+    params.D[0]    = De
+    params.D[1]    = Di
+    params.D[2]    = Dm
+
+    params.mu[0]   = mue
+    params.mu[1]   = mui
+    params.mu[2]   = mum
+
+    params.A[:]    = Ck[:]
+    params.B[:]    = B[:]
+    params.C[:]    = A[:]
+
+    # Account for the 2/3 term to convert from electron temperature to electron energy
+    for i in range(len(params.A)):
+        params.A[i] *= (2/3)**(params.B[i])
+
+    params.dH[:]   = dH[:]
+    params.dEps[:] = dEps[:]
+    params.qStar   = qStar
+    params.alpha   = alpha
+    params.ks      = ks
+    params.gam     = gam
+    params.kappaB  = kappaB
+    params.nAronp0 = nAr / np0
+    params.p0      = p0
+    params.Tg0     = Tg0
+    params.EC      = 2.0 * me / mAr \
+        * np.sqrt(16.0 * (me + mAr) * e0 * c**2
+                  / (3.0 * np.pi * me * mAr)) * se * nAr * tau
+    # params.EC = 2.0 * me / mAr * 3.8e9 * tau
+
+    params.verticalShift = verticalShift / V0
+
+    # Parameters needed to compute the current with dimensions
+    params.V0Ltau  = V0 / (L * tau)
+    params.V0L     = V0 / L
+    params.LLV0tau = (L*L) / (V0*tau)
+    params.tauL    = L / tau
+    params.np0     = np0           # "nominal" electron density [1/m^3]
+    params.qe      = qe            # unit charge [C]
+    params.eps0    = eps0          # unit charge [C]
+    params.eArea   = electrodeArea # electrode area [m^2]
+
+
+    reactionExpressionTypelist =  np.array([False,False,False,False,False,False,False,False,
+                                           True,True,True,True,True,True,True,True,True,True,True,True,True,True,
+                                           False,False,True,True,True,True,False,False,False,False,True,True])
+
+    thresholded_rxn = np.array([False, False, False, False, False, False, False, False,
+                                True, True, True, True, False, True, True, True, True, False, False, False, False,
+                                True, False, False, False, False, False, False, False, False, False, False, True,
+                                False])
+    reactionsList = []
+    LOGFilename = 'interpolationSample%s.log'%str(iSample)
+    f = open(LOGFilename, 'w')
+
+    for i in range(Nr):
+        sample_root_dir = "../../../BOLSIGChemistry_6SpeciesRates"
+        if reactionExpressionTypelist[i]:
+            if i < 14 or i == 16 or i == 19 or i > 23:
+                fileString = sample_root_dir + "/" + rxnNameDict[i]
+                fileName = "%s.%08d.h5" % (fileString, iSample)
+                f = h5.File(fileName, 'r')
+                dataset = f["table"]
+            else:
+                fileString = sample_root_dir + "/" + "StepwiseExcitations"
+                fileName = "%s.%08d.h5" % (fileString, iSample)
+                f = h5.File(fileName, 'r')
+                dataset = f[rxnNameDict[i]]
+            
+            rateCoeff = dataset[:,1]
+            if i > 23 and i < 28:
+                rateCoeff /= 6.022e23**2
+            else:
+                rateCoeff /= 6.022e23
+            Te = dataset[:,0]
+            Te /= 11604.
+
+            ## Removing BOLSIG failures
+            fail_inds = []
+            for j in range(len(rateCoeff)):
+                if rateCoeff[j] == 0.0 and j > np.nonzero(rateCoeff)[0][0]:
+                    fail_inds.append(j)
+
+            Te = np.delete(Te, fail_inds)
+            rateCoeff = np.delete(rateCoeff, fail_inds)
+
+            # Sorting mean energy array and rate coefficient array based on
+            # the mean energy array.
+            Teinds = Te.argsort()
+            rateCoeff = rateCoeff[Teinds]
+            Te = Te[Teinds]
+
+            # Find duplicates
+            TeDuplicateinds = np.where(np.abs(np.diff(Te, axis=0)) > 0.0)
+            TeDuplicateindsForLog = np.where(np.abs(np.diff(Te, axis=0)) == 0.0)
+            rateCoeff = rateCoeff[TeDuplicateinds]
+            Te = Te[TeDuplicateinds]
+
+            # Nondimensionalization of mean energy.
+            Te *= 1.5
+
+            # Find first non-zero value of the coefficient rate.
+            I = np.nonzero(rateCoeff)
+
+            diffRateCoeff = [j-i for i, j in zip(rateCoeff[:-1], rateCoeff[1:])]
+            diffTe = [j-i for i, j in zip(Te[:-1], Te[1:])]
+
+            Monotonicity = np.asarray([j/i for i, j in zip(diffTe, diffRateCoeff)])
+            Monotonicity = np.insert(Monotonicity, 0, 0.0, axis=0)
+
+            Nan = np.isnan(Monotonicity)
+            Inf = np.isinf(Monotonicity)
+            if thresholded_rxn[i] == True:
+                indexPositive = np.where(Monotonicity>0.0)
+            else:
+                indexPositive = np.where(Monotonicity<0.0)
+            Positive = np.full(Monotonicity.shape, False, dtype=bool)
+            Positive[indexPositive] = True
+
+            indices = Nan + Inf + Positive
+
+            #lastFalse = np.where(indices==False)[-1][-1] + 2
+            for k in range(len(Te)):
+                if (Te[k] < 4.5 and indices[k] == False):
+                    lastFalse = k + 2
+
+            # Transformation to log scale.
+            TeLog = np.log(Te)
+
+            # Compute the slope of the rate coefficient between its first two non-zero values.
+            # Finite differences are used.
+            dydx = (rateCoeff[lastFalse + 1] - rateCoeff[lastFalse]) \
+                 / (Te[lastFalse + 1] - Te[lastFalse])
+
+            # Arrhenius form: kf = A * exp(-C / Te)
+            C = Te[lastFalse]**2.0*dydx / rateCoeff[lastFalse]
+
+            # Compute pre-exponential coefficient, A, in log scale.
+            ALog = np.log(rateCoeff[lastFalse]) + C / Te[lastFalse]
+
+            # Transform rate coefficient in log scale.
+            rateCoeffLog = np.zeros(rateCoeff.shape)
+            rateCoeffLog[lastFalse:] = np.log(rateCoeff[lastFalse:])
+            # For the troublesome values, we use the Arrhenius form.
+            rateCoeffLog[0:lastFalse] = ALog - C / Te[0:lastFalse]
+            # Nondimensionalization in log scale.
+            if i < 12:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
+            elif i > 23 and i < 28:
+                rateCoeffLog += - np.log(1.0/tau) + 2*np.log(np0)
+            else:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(np0)
+
+            # Interpolation in log scale.
+            reactionExpressionsLog = CubicSpline(TeLog, rateCoeffLog)
+            # Gradient in log scale
+            reactionTExpressionsLog = CubicSpline.derivative(reactionExpressionsLog)
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf_log = reactionExpressionsLog,
+                                kf_T_log = reactionTExpressionsLog)
+            reactionsList.append(reaction)
+
+            logging.basicConfig(filename=LOGFilename)
+            logging.warning('Interpolation info (Reaction %s):', i)
+            logging.warning('First non-zero entry in the rate coefficient: %s', I[0][0])
+            logging.warning('Monotonicity of the rate coefficient start from entry: %s', lastFalse)
+            logging.warning('Position of possible duplicates in mean energy array: %s',
+                          TeDuplicateindsForLog[0])
+
+        else:
+            fileName = "%s/Arrhenius.%08d.h5" % (sample_root_dir, iSample)
+            f = h5.File(fileName, 'r')
+            arrh_Coeffs = f[rxnNameDict[i]][...]
+            A, B, C = arrh_Coeffs
+
+            # Non-dimensionalize the coefficients
+            if i < 4:
+                A /= 6.022e23
+                A *= tau*np0
+            elif i == 4:
+                A /= 6.022e23
+                A *= tau*nAr
+            elif i > 4 and i < 8:
+                A *= tau
+            elif i > 27 and i < 30:
+                A /= 6.022e23
+                A *= tau*nAr
+            else:
+                A /= 6.022e23
+                A *= tau*np0
+
+            A *= ((2./3.)*11604)**B 
+            C *= 1.5/(e0*11604)
+
+            rxn = eval("lambda energy :" + f"{A} * energy**{B} * np.exp(-{C} / energy)")
+            rxn_T = eval("lambda energy :" + f"{A} * energy**({B}-1) * np.exp(-{C} / energy) * ({B} + {C} / energy)")
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf = rxn, kf_T = rxn_T)
+            reactionsList.append(reaction)
+
+    params.reactionsList = reactionsList
+
+    diffList = []
+    # Data from BOLSIG
+    # Te in [eV]
+    # De * N in [1/(m*s)]
+    transport = h5.File("../../../BOLSIGChemistry_6SpeciesRates/Transport.%08d.h5" % (iSample), 'r')
+    NDe_v_Te = transport["diffusivity"]
+    Te_trans = NDe_v_Te[:,0]
+    Te_trans /= 11604
+    De_interp = (NDe_v_Te[:,1]/nAr)*tau/(L*L)
+    De_spline = CubicSpline(Te_trans, De_interp)
+    De_Te_spline = CubicSpline.derivative(De_spline)
+    diffusivity = Diffusivity(interpolate = True, D_expression = De_spline, D_T_expression = De_Te_spline)
+    diffList.append(diffusivity)
+
+    Ns = 6
+    for i in range(1, Ns):
+        diffList.append(Diffusivity(interpolate = False))
+
+    params.diffusivityList = diffList
+
+    muList = []
+    # Data from BOLSIG
+    # Te in [eV]
+    # Mue * N in [1/(V*m*s)]
+    Nmue_v_Te = transport["mobility"]
+    mue_interp = (Nmue_v_Te[:,1]/nAr)*V0*tau/(L*L)
+    mue_spline = CubicSpline(Te_trans, mue_interp)
+    mue_Te_spline = CubicSpline.derivative(mue_spline)
+    mobility = Mobility(interpolate = True, mu_expression = mue_spline, mu_T_expression = mue_Te_spline)
+    muList.append(mobility)
+
+    Ns = 6
+    for i in range(1, Ns):
+        muList.append(Mobility(interpolate = False))
+
+    params.mobilityList = muList
+
+    # 5) Dump to screen
+    params.print()
diff --git a/psaapProperties_6Species_Sampling_10Torr_Expanded.py b/psaapProperties_6Species_Sampling_10Torr_Expanded.py
new file mode 100755
index 000000000..f85cb08ca
--- /dev/null
+++ b/psaapProperties_6Species_Sampling_10Torr_Expanded.py
@@ -0,0 +1,494 @@
+import numpy as np
+from scipy.interpolate import CubicSpline
+import csv
+import matplotlib.pyplot as plt
+import matplotlib.colors as mcolors
+import h5py as h5
+import logging
+
+class Reaction(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Diffusivity(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Mobility(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+
+def setPsaapProperties_6Species_Sampling_10Torr_Expanded(gam, inputV0, inputVDC, params, Nr, iSample):
+    """Sets non-dimensional properties corresponding to Liu 2014 paper.
+
+    Inputs:
+      gam : Secondary electron emission coefficient
+      params : chebSolver.modelParams class
+
+    Outputs: None
+      params data is overwritten using values from Liu 2014.
+    """
+    ###################################################################
+    # User specified parameters (you may change these if you wish to
+    # run a different scenario from Liu 2014)
+    ###################################################################
+
+    # densities
+    nAr = 3.22e23     # background number density of Ar [1/m^3] (corresponds to p = 10 Torr)
+    np0 = 8e16        # "nominal" electron density [1/m^3]
+
+    # masses
+    # me = 9.10938356e-31        # mass of an electron [kg]
+    # me = 5.489e-4              # mass of an electron [u]
+    me = 0.511e6                 # mass of an electron [eV/c2]
+    # mAr = 39.948               # mass of an argon atom [u]
+    # mAr = 39.948 * 1.66054e-27 # mass of an argon atom [kg]
+    mAr = 37.2158e9              # mass of an electron [eV/c2]
+    # u = 931.4941e6             # eV/c2
+    c = 299792458                # speed of light [m/s]
+    se = 40                      # momentum cross section [A^2]
+
+    # nominal electron energy
+    e0 = 1.0  # [eV]
+
+    # pressure
+    p  = 1333.3*1.5      # [J/m^3] *1.5 to convert it to energy (10 Torr)
+
+    # gas energy at the wall
+    Tg0 = 0.038778    # 3/2*300K*kB ~ (p0 - nT[:,0])/ntot
+
+    # characteristics of driving voltage
+    V0  = inputV0                 # amplitude of driving voltage [V]
+    verticalShift = inputVDC      # DC voltage (vertical shift in driving voltage)
+    tau = (1./13.56e6)             # period of driving voltage [s]
+    L   = 2.00*0.005              # half-gap-width [m] (gap width is 2 cm)
+    electrodeArea = np.pi*0.05**2 # electrode area [m^2] (electrode diameter = 0.1 m)
+
+    # Add voltage uncertainty
+    V0 += h5.File('../../../BOLSIGChemistry_Voltage/Voltage.%08d.h5' % (iSample), 'r')["V_Err"][0]
+
+    # transport parameters
+    nmue = 9.66e21   # argon number density times electron mobility [1/(V*cm*s)]
+    nmum = 0.0
+    nmui = 8.0e19
+    nDe  = 3.86e22   # argon number density times electron diffusivity [1/(cm*s)]
+    nDi  = 2.07e18   # argon number density times ion diffusivity [1/(cm*s)]
+    nDm  = 2.42e18   # argon number density times metastable diffusivity [1/(cm*s)]
+
+    # reaction parameters (NB: k_i = Ck*Ee^B*exp(-A/Ee))
+    #                          Ee = 3/2*Te (Te in eV)
+    #                          -> k_i = [Ck*(2/3)^B] * Ee^B * exp[-(3/2)*A/Ee]
+    # nominal
+    Ck = np.array([2.0e-13,2.1e-15,5.0e-16,6.4e-16,2.1e-21,1.32e8,1.72e7,1.50e7,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0e-18,4.0e-19,0.0,0.0,0.0,0.0,2.5e-17,2.5e-17,1.0e-15,1.0e-15,0.0,0.0]) # pre-exponential factors [m^3/s]
+    B  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-0.5,0,0,0,0,0,0,0,0,0,0])
+    A  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]) # activation temperature [eV]
+    dH = np.array([0.0,-7.541,-10.577,-7.393,0.0,0.0,0.0,0.0,11.577,11.725,13.168,15.76,-11.577,4.183,0.148,1.592,2.592,-1.444,-1.592,-11.725,-0.148,1.444,0.0,0.0,-4.183,-4.035,-2.592,-15.76,0.0,0.0,-8.985,-9.133,4.035,-13.168]) # energy lost per electron due to ionization rxn [eV]
+    dEps = np.array([0.0,15.76,11.577,11.725,13.168,0.0])
+
+    # BC parameters
+    # ks = 1.19e7  # electron recombination rate [cm/s]
+    ks = 1.366109824889323e7 # electron recombination rate [cm/s/eV]
+
+    ###################################################################
+    # Constants of nature (probably shouldn't change unless you have
+    # root privileges on universe)
+    ###################################################################
+    qe   = 1.6e-19   # unit charge [C]
+    eps0 = 8.86e-12  # permittivity of free space [F/m]
+    kB   = 1.38e-23  # Boltzmann constant [J/K]
+    # kB   = 8.62e−5 # Boltzmann constant [eV/K]
+
+
+    ###################################################################
+    # Calculate non-dimensional parameters
+    ###################################################################
+
+    # 1) Convert input units to base SI (except eV)
+    nDe  *= 100. # 1/(m*s)
+    nDi  *= 100. # 1/(m*s)
+    nDm  *= 100.
+    nmue *= 100. # 1/(V*m*s)
+    nmui *= 100. # 1/(V*m*s)
+    ks   *= 0.01 # m/s
+    se   *= 1.0e-20  # m^2
+
+    # 2) Compute "raw" transport parameters
+    De  = nDe/nAr
+    Di  = nDi/nAr
+    Dm  = nDm/nAr
+
+    mue = nmue/nAr
+    mui = nmui/nAr
+    mum = nmum/nAr
+
+    # 3) Compute non-dimensional properties required by solver
+    De    = De*tau/(L*L)
+    Di    = Di*tau/(L*L)
+    Dm    = Dm*tau/(L*L)
+
+    mue   = mue*V0*tau/(L*L)
+    mui   = mui*V0*tau/(L*L)
+    mum   = mum*V0*tau/(L*L)
+
+    Ck[0:4]  *= tau*np0
+    Ck[4]    *= tau*nAr
+    Ck[5:8]  *= tau
+    Ck[8:12] *= tau*nAr
+    Ck[12:24]  *= tau*np0
+    Ck[24:28] *= tau*np0*np0
+    Ck[28:30] *= tau*nAr
+    Ck[30:] *= tau*np0
+    A       = A*1.5/e0  # 1.5 to convert from temperature to energy
+    dH      = dH/e0
+    qStar   = V0/e0 # qe*V0/e0, since e0 in eV, need qe*V0 in eV, which is just V0 in V
+    alpha   = qe*np0*L*L/(V0*eps0)
+    ks      = ks*tau/L
+    p0      = p/qe/np0
+    kappaB  = 4.878171165833662*1.6129 # non-dimensional thermal conductivity of background specie
+                                # (2/3)*tau/L**2*Kb/np0/kB,
+                                # where Kb is the thermal conductivity of background specie
+
+    params.beta = np.array([[0,1,1,1,0,0,0,0,1,1,1,2,1,2,1,1,2,1,1,1,1,1,0,0,1,1,1,1,0,0,1,1,2,1],                     # E
+                            [0,1,1,1,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0],                     # AR+
+                            [0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,1,0,0,0,0,0],                     # AR(m)
+                            [0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0],                     # AR(r)
+                            [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0],                     # AR(4p)
+                            [2,1,1,1,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1]], dtype=np.int64)    # AR
+
+    params.alfa = np.array([[0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,0,0,0,0,1,1],                     # E
+                            [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0],                     # AR+
+                            [2,1,0,2,1,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],                     # AR(m)
+                            [0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,1,0],                     # AR(r)
+                            [0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,0,1],                     # AR(4p)
+                            [0,0,0,0,1,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0]], dtype=np.int64)    # AR
+    
+    # Rxn1:  2AR(m)         ->   2AR
+    # Rxn2:  AR(m) + AR(r)  ->   E + AR+ + AR
+    # Rxn3:  2AR(4p)        ->   E + AR+ + AR
+    # Rxn4:  2AR(m)         ->   E + AR+ + AR
+    # Rxn5:  AR(m) + AR     ->   2AR
+    # Rxn6:  AR(r)          ->   AR + hv
+    # Rxn7:  AR(4p)         ->   AR(m) + hv
+    # Rxn8:  AR(4p)         ->   AR(r) + hv
+    # Rxn9:  E + AR         ->   E + AR(m)
+    # Rxn10: E + AR         ->   E + AR(r)
+    # Rxn11: E + AR         ->   E + AR(4p)
+    # Rxn12: E + AR         ->   2E + AR+
+    # Rxn13: E + AR(m)      ->   E + AR
+    # Rxn14: E + AR(m)      ->   2E + AR+
+    # Rxn15: E + AR(m)      ->   E + AR(r)
+    # Rxn16: E + AR(m)      ->   E + AR(4p)
+    # Rxn17: E + AR(4p)     ->   2E + AR+
+    # Rxn18: E + AR(4p)     ->   E + AR(r)
+    # Rxn19: E + AR(4p)     ->   E + AR(m)
+    # Rxn20: E + AR(r)      ->   E + AR
+    # Rxn21: E + AR(r)      ->   E + AR(m)
+    # Rxn22: E + AR(r)      ->   E + AR(4p)
+    # Rxn23: E + AR+        ->   AR(m) + hv
+    # Rxn24: E + AR+        ->   AR(4p) + hv
+    # Rxn25: 2E + AR+       ->   E + AR(m)
+    # Rxn26: 2E + AR+       ->   E + AR(r)
+    # Rxn27: 2E + AR+       ->   E + AR(4p)
+    # Rxn28: 2E + AR+       ->   E + AR
+    # Rxn29: AR + AR(4p)    ->   AR + AR(m)
+    # Rxn30: AR + AR(4p)    ->   AR + AR(r)
+    # Rxn31: AR(m) + AR(4p) ->   E + AR+ + AR
+    # Rxn32: AR(r) + AR(4p) ->   E + AR+ + AR
+    # Rxn33: E + AR(r)      ->   2E + AR+
+    # Rxn34: E + AR(4p)     ->   E + AR 
+
+    rxnNameDict = {0: "Ar(m) + Ar(m) => Ar + Ar",
+                   1: "Ar(m) + Ar(r) => E + Ar + Ar+",
+                   2: "Ar(4p) + Ar(4p) => E + Ar + Ar+",
+                   3: "Ar(m) + Ar(m) => E + Ar + Ar+",
+                   4: "Ar + Ar(m) => Ar + Ar",
+                   5: "Ar(r) => Ar",
+                   6: "Ar(4p) => Ar(m)",
+                   7: "Ar(4p) => Ar(r)",
+                   8: "Excitation_Metastable",
+                   9: "Excitation_Resonant",
+                   10: "Excitation_4p",
+                   11: "Ionization",
+                   12: "DeExcitation_Metastable",
+                   13: "StepIonization_Metastable",
+                   14: "E + Ar(m) => E + Ar(r)",
+                   15: "E + Ar(m) => E + Ar(4p)",
+                   16: "StepIonization_4p",
+                   17: "E + Ar(4p) => E + Ar(r)",
+                   18: "E + Ar(4p) => E + Ar(m)",
+                   19: "DeExcitation_Resonant",
+                   20: "E + Ar(r) => E + Ar(m)",
+                   21: "E + Ar(r) => E + Ar(4p)",
+                   22: "E + Ar+ => Ar(m)",
+                   23: "E + Ar+ => Ar(4p)",
+                   24: "3BdyRecomb_Metastable",
+                   25: "3BdyRecomb_Resonant",
+                   26: "3BdyRecomb_4p",
+                   27: "3BdyRecomb_Ground",
+                   28: "Ar + Ar(4p) => Ar + Ar(m)",
+                   29: "Ar + Ar(4p) => Ar + Ar(r)",
+                   30: "Ar(m) + Ar(4p) => E + Ar+ + Ar",
+                   31: "Ar(r) + Ar(4p) => E + Ar+ + Ar",
+                   32: "StepIonization_Resonant",
+                   33: "DeExcitation_4p"}
+
+    # 4) Set values in params class
+    params.D[0]    = De
+    params.D[1]    = Di
+    params.D[2]    = Dm
+
+    params.mu[0]   = mue
+    params.mu[1]   = mui
+    params.mu[2]   = mum
+
+    params.A[:]    = Ck[:]
+    params.B[:]    = B[:]
+    params.C[:]    = A[:]
+
+    # Account for the 2/3 term to convert from electron temperature to electron energy
+    for i in range(len(params.A)):
+        params.A[i] *= (2/3)**(params.B[i])
+
+    params.dH[:]   = dH[:]
+    params.dEps[:] = dEps[:]
+    params.qStar   = qStar
+    params.alpha   = alpha
+    params.ks      = ks
+    params.gam     = gam
+    params.kappaB  = kappaB
+    params.nAronp0 = nAr / np0
+    params.p0      = p0
+    params.Tg0     = Tg0
+    params.EC      = 2.0 * me / mAr \
+        * np.sqrt(16.0 * (me + mAr) * e0 * c**2
+                  / (3.0 * np.pi * me * mAr)) * se * nAr * tau
+    # params.EC = 2.0 * me / mAr * 3.8e9 * tau
+
+    params.verticalShift = verticalShift / V0
+
+    # Parameters needed to compute the current with dimensions
+    params.V0Ltau  = V0 / (L * tau)
+    params.V0L     = V0 / L
+    params.LLV0tau = (L*L) / (V0*tau)
+    params.tauL    = L / tau
+    params.np0     = np0           # "nominal" electron density [1/m^3]
+    params.qe      = qe            # unit charge [C]
+    params.eps0    = eps0          # unit charge [C]
+    params.eArea   = electrodeArea # electrode area [m^2]
+
+
+    reactionExpressionTypelist =  np.array([False,False,False,False,False,False,False,False,
+                                           True,True,True,True,True,True,True,True,True,True,True,True,True,True,
+                                           False,False,True,True,True,True,False,False,False,False,True,True])
+
+    thresholded_rxn = np.array([False, False, False, False, False, False, False, False,
+                                True, True, True, True, False, True, True, True, True, False, False, False, False,
+                                True, False, False, False, False, False, False, False, False, False, False, True,
+                                False])
+    reactionsList = []
+    LOGFilename = 'interpolationSample%s.log'%str(iSample)
+    f = open(LOGFilename, 'w')
+
+    for i in range(Nr):
+        sample_root_dir = "../../../BOLSIGChemistry_6SpeciesRates"
+        if reactionExpressionTypelist[i]:
+            if i < 14 or i == 16 or i == 19 or i > 23:
+                fileString = sample_root_dir + "/" + rxnNameDict[i]
+                fileName = "%s.%08d.h5" % (fileString, iSample)
+                f = h5.File(fileName, 'r')
+                dataset = f["table"]
+            else:
+                fileString = sample_root_dir + "/" + "StepwiseExcitations"
+                fileName = "%s.%08d.h5" % (fileString, iSample)
+                f = h5.File(fileName, 'r')
+                dataset = f[rxnNameDict[i]]
+            
+            rateCoeff = dataset[:,1]
+            if i > 23 and i < 28:
+                rateCoeff /= 6.022e23**2
+            else:
+                rateCoeff /= 6.022e23
+            Te = dataset[:,0]
+            Te /= 11604.
+
+            ## Removing BOLSIG failures
+            fail_inds = []
+            for j in range(len(rateCoeff)):
+                if rateCoeff[j] == 0.0 and j > np.nonzero(rateCoeff)[0][0]:
+                    fail_inds.append(j)
+
+            Te = np.delete(Te, fail_inds)
+            rateCoeff = np.delete(rateCoeff, fail_inds)
+
+            # Sorting mean energy array and rate coefficient array based on
+            # the mean energy array.
+            Teinds = Te.argsort()
+            rateCoeff = rateCoeff[Teinds]
+            Te = Te[Teinds]
+
+            # Find duplicates
+            TeDuplicateinds = np.where(np.abs(np.diff(Te, axis=0)) > 0.0)
+            TeDuplicateindsForLog = np.where(np.abs(np.diff(Te, axis=0)) == 0.0)
+            rateCoeff = rateCoeff[TeDuplicateinds]
+            Te = Te[TeDuplicateinds]
+
+            # Nondimensionalization of mean energy.
+            Te *= 1.5
+
+            # Find first non-zero value of the coefficient rate.
+            I = np.nonzero(rateCoeff)
+
+            diffRateCoeff = [j-i for i, j in zip(rateCoeff[:-1], rateCoeff[1:])]
+            diffTe = [j-i for i, j in zip(Te[:-1], Te[1:])]
+
+            Monotonicity = np.asarray([j/i for i, j in zip(diffTe, diffRateCoeff)])
+            Monotonicity = np.insert(Monotonicity, 0, 0.0, axis=0)
+
+            Nan = np.isnan(Monotonicity)
+            Inf = np.isinf(Monotonicity)
+            if thresholded_rxn[i] == True:
+                indexPositive = np.where(Monotonicity>0.0)
+            else:
+                indexPositive = np.where(Monotonicity<0.0)
+            Positive = np.full(Monotonicity.shape, False, dtype=bool)
+            Positive[indexPositive] = True
+
+            indices = Nan + Inf + Positive
+
+            #lastFalse = np.where(indices==False)[-1][-1] + 2
+            for k in range(len(Te)):
+                if (Te[k] < 4.5 and indices[k] == False):
+                    lastFalse = k + 2
+
+            # Transformation to log scale.
+            TeLog = np.log(Te)
+
+            # Compute the slope of the rate coefficient between its first two non-zero values.
+            # Finite differences are used.
+            dydx = (rateCoeff[lastFalse + 1] - rateCoeff[lastFalse]) \
+                 / (Te[lastFalse + 1] - Te[lastFalse])
+
+            # Arrhenius form: kf = A * exp(-C / Te)
+            C = Te[lastFalse]**2.0*dydx / rateCoeff[lastFalse]
+
+            # Compute pre-exponential coefficient, A, in log scale.
+            ALog = np.log(rateCoeff[lastFalse]) + C / Te[lastFalse]
+
+            # Transform rate coefficient in log scale.
+            rateCoeffLog = np.zeros(rateCoeff.shape)
+            rateCoeffLog[lastFalse:] = np.log(rateCoeff[lastFalse:])
+            # For the troublesome values, we use the Arrhenius form.
+            rateCoeffLog[0:lastFalse] = ALog - C / Te[0:lastFalse]
+            # Nondimensionalization in log scale.
+            if i < 12:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
+            elif i > 23 and i < 28:
+                rateCoeffLog += - np.log(1.0/tau) + 2*np.log(np0)
+            else:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(np0)
+
+            # Interpolation in log scale.
+            reactionExpressionsLog = CubicSpline(TeLog, rateCoeffLog)
+            # Gradient in log scale
+            reactionTExpressionsLog = CubicSpline.derivative(reactionExpressionsLog)
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf_log = reactionExpressionsLog,
+                                kf_T_log = reactionTExpressionsLog)
+            reactionsList.append(reaction)
+
+            logging.basicConfig(filename=LOGFilename)
+            logging.warning('Interpolation info (Reaction %s):', i)
+            logging.warning('First non-zero entry in the rate coefficient: %s', I[0][0])
+            logging.warning('Monotonicity of the rate coefficient start from entry: %s', lastFalse)
+            logging.warning('Position of possible duplicates in mean energy array: %s',
+                          TeDuplicateindsForLog[0])
+
+        else:
+            fileName = "%s/Arrhenius.%08d.h5" % (sample_root_dir, iSample)
+            f = h5.File(fileName, 'r')
+            arrh_Coeffs = f[rxnNameDict[i]][...]
+            A, B, C = arrh_Coeffs
+
+            # Non-dimensionalize the coefficients
+            if i < 4:
+                A /= 6.022e23
+                A *= tau*np0
+            elif i == 4:
+                A /= 6.022e23
+                A *= tau*nAr
+            elif i > 4 and i < 8:
+                A *= tau
+            elif i > 27 and i < 30:
+                A /= 6.022e23
+                A *= tau*nAr
+            else:
+                A /= 6.022e23
+                A *= tau*np0
+
+            A *= ((2./3.)*11604)**B 
+            C *= 1.5/(e0*11604)
+
+            rxn = eval("lambda energy :" + f"{A} * energy**{B} * np.exp(-{C} / energy)")
+            rxn_T = eval("lambda energy :" + f"{A} * energy**({B}-1) * np.exp(-{C} / energy) * ({B} + {C} / energy)")
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf = rxn, kf_T = rxn_T)
+            reactionsList.append(reaction)
+
+    params.reactionsList = reactionsList
+
+    diffList = []
+    # Data from BOLSIG
+    # Te in [eV]
+    # De * N in [1/(m*s)]
+    transport = h5.File("../../../BOLSIGChemistry_6SpeciesRates/Transport.%08d.h5" % (iSample), 'r')
+    NDe_v_Te = transport["diffusivity"]
+    Te_trans = NDe_v_Te[:,0]
+    Te_trans /= 11604
+    De_interp = (NDe_v_Te[:,1]/nAr)*tau/(L*L)
+    De_spline = CubicSpline(Te_trans, De_interp)
+    De_Te_spline = CubicSpline.derivative(De_spline)
+    diffusivity = Diffusivity(interpolate = True, D_expression = De_spline, D_T_expression = De_Te_spline)
+    diffList.append(diffusivity)
+
+    Ns = 6
+    for i in range(1, Ns):
+        diffList.append(Diffusivity(interpolate = False))
+
+    params.diffusivityList = diffList
+
+    muList = []
+    # Data from BOLSIG
+    # Te in [eV]
+    # Mue * N in [1/(V*m*s)]
+    Nmue_v_Te = transport["mobility"]
+    mue_interp = (Nmue_v_Te[:,1]/nAr)*V0*tau/(L*L)
+    mue_spline = CubicSpline(Te_trans, mue_interp)
+    mue_Te_spline = CubicSpline.derivative(mue_spline)
+    mobility = Mobility(interpolate = True, mu_expression = mue_spline, mu_T_expression = mue_Te_spline)
+    muList.append(mobility)
+
+    Ns = 6
+    for i in range(1, Ns):
+        muList.append(Mobility(interpolate = False))
+
+    params.mobilityList = muList
+
+    # 5) Dump to screen
+    params.print()
diff --git a/psaapProperties_6Species_Sampling_1Torr_Expanded.py b/psaapProperties_6Species_Sampling_1Torr_Expanded.py
new file mode 100755
index 000000000..e3301d381
--- /dev/null
+++ b/psaapProperties_6Species_Sampling_1Torr_Expanded.py
@@ -0,0 +1,495 @@
+import numpy as np
+from scipy.interpolate import CubicSpline
+import csv
+import matplotlib.pyplot as plt
+import matplotlib.colors as mcolors
+import h5py as h5
+import logging
+
+class Reaction(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Diffusivity(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Mobility(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+
+def setPsaapProperties_6Species_Sampling_1Torr_Expanded(gam, inputV0, inputVDC, params, Nr, iSample):
+    """Sets non-dimensional properties corresponding to Liu 2014 paper.
+
+    Inputs:
+      gam : Secondary electron emission coefficient
+      params : chebSolver.modelParams class
+
+    Outputs: None
+      params data is overwritten using values from Liu 2014.
+    """
+    ###################################################################
+    # User specified parameters (you may change these if you wish to
+    # run a different scenario from Liu 2014)
+    ###################################################################
+
+    # densities
+    nAr = 3.22e22     # background number density of Ar [1/m^3] (corresponds to p = 1 Torr)
+    np0 = 8e16        # "nominal" electron density [1/m^3]
+
+    # masses
+    # me = 9.10938356e-31        # mass of an electron [kg]
+    # me = 5.489e-4              # mass of an electron [u]
+    me = 0.511e6                 # mass of an electron [eV/c2]
+    # mAr = 39.948               # mass of an argon atom [u]
+    # mAr = 39.948 * 1.66054e-27 # mass of an argon atom [kg]
+    mAr = 37.2158e9              # mass of an electron [eV/c2]
+    # u = 931.4941e6             # eV/c2
+    c = 299792458                # speed of light [m/s]
+    se = 40                      # momentum cross section [A^2]
+
+    # nominal electron energy
+    e0 = 1.0  # [eV]
+
+    # pressure
+    p  = 133.3*1.5      # [J/m^3] *1.5 to convert it to energy (1 Torr)
+
+    # gas energy at the wall
+    Tg0 = 0.038778    # 3/2*300K*kB ~ (p0 - nT[:,0])/ntot
+
+    # characteristics of driving voltage
+    V0  = inputV0                 # amplitude of driving voltage [V]
+    verticalShift = inputVDC      # DC voltage (vertical shift in driving voltage)
+    tau = (1./13.56e6)             # period of driving voltage [s]
+    L   = 2.00*0.005              # half-gap-width [m] (gap width is 2 cm)
+    electrodeArea = np.pi*0.05**2 # electrode area [m^2] (electrode diameter = 0.1 m)
+
+    # Add voltage uncertainty
+    V0 += h5.File('../../../BOLSIGChemistry_Voltage/Voltage.%08d.h5' % (iSample), 'r')["V_Err"][0]
+
+    # transport parameters
+    nmue = 9.66e21   # argon number density times electron mobility [1/(V*cm*s)]
+    nmum = 0.0
+    nmui = 8.0e19
+    nDe  = 3.86e22   # argon number density times electron diffusivity [1/(cm*s)]
+    nDi  = 2.07e18   # argon number density times ion diffusivity [1/(cm*s)]
+    nDm  = 2.42e18   # argon number density times metastable diffusivity [1/(cm*s)]
+
+    # reaction parameters (NB: k_i = Ck*Ee^B*exp(-A/Ee))
+    #                          Ee = 3/2*Te (Te in eV)
+    #                          -> k_i = [Ck*(2/3)^B] * Ee^B * exp[-(3/2)*A/Ee]
+    # nominal
+    Ck = np.array([2.0e-13,2.1e-15,5.0e-16,6.4e-16,2.1e-21,1.32e8,1.72e7,1.50e7,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0e-18,4.0e-19,0.0,0.0,0.0,0.0,2.5e-17,2.5e-17,1.0e-15,1.0e-15,0.0,0.0]) # pre-exponential factors [m^3/s]
+    B  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-0.5,0,0,0,0,0,0,0,0,0,0])
+    A  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]) # activation temperature [eV]
+    dH = np.array([0.0,-7.541,-10.577,-7.393,0.0,0.0,0.0,0.0,11.577,11.725,13.168,15.76,-11.577,4.183,0.148,1.592,2.592,-1.444,-1.592,-11.725,-0.148,1.444,0.0,0.0,-4.183,-4.035,-2.592,-15.76,0.0,0.0,-8.985,-9.133,4.035,-13.168]) # energy lost per electron due to ionization rxn [eV]
+    dEps = np.array([0.0,15.76,11.577,11.725,13.168,0.0])
+
+    # BC parameters
+    # ks = 1.19e7  # electron recombination rate [cm/s]
+    ks = 1.366109824889323e7 # electron recombination rate [cm/s/eV]
+
+    ###################################################################
+    # Constants of nature (probably shouldn't change unless you have
+    # root privileges on universe)
+    ###################################################################
+    qe   = 1.6e-19   # unit charge [C]
+    eps0 = 8.86e-12  # permittivity of free space [F/m]
+    kB   = 1.38e-23  # Boltzmann constant [J/K]
+    # kB   = 8.62e−5 # Boltzmann constant [eV/K]
+
+
+    ###################################################################
+    # Calculate non-dimensional parameters
+    ###################################################################
+
+    # 1) Convert input units to base SI (except eV)
+    nDe  *= 100. # 1/(m*s)
+    nDi  *= 100. # 1/(m*s)
+    nDm  *= 100.
+    nmue *= 100. # 1/(V*m*s)
+    nmui *= 100. # 1/(V*m*s)
+    ks   *= 0.01 # m/s
+    se   *= 1.0e-20  # m^2
+
+    # 2) Compute "raw" transport parameters
+    De  = nDe/nAr
+    Di  = nDi/nAr
+    Dm  = nDm/nAr
+
+    mue = nmue/nAr
+    mui = nmui/nAr
+    mum = nmum/nAr
+
+    # 3) Compute non-dimensional properties required by solver
+    De    = De*tau/(L*L)
+    Di    = Di*tau/(L*L)
+    Dm    = Dm*tau/(L*L)
+
+    mue   = mue*V0*tau/(L*L)
+    mui   = mui*V0*tau/(L*L)
+    mum   = mum*V0*tau/(L*L)
+
+    Ck[0:4]  *= tau*np0
+    Ck[4]    *= tau*nAr
+    Ck[5:8]  *= tau
+    Ck[8:12] *= tau*nAr
+    Ck[12:24]  *= tau*np0
+    Ck[24:28] *= tau*np0*np0
+    Ck[28:30] *= tau*nAr
+    Ck[30:] *= tau*np0
+    A       = A*1.5/e0  # 1.5 to convert from temperature to energy
+    dH      = dH/e0
+    qStar   = V0/e0 # qe*V0/e0, since e0 in eV, need qe*V0 in eV, which is just V0 in V
+    alpha   = qe*np0*L*L/(V0*eps0)
+    ks      = ks*tau/L
+    p0      = p/qe/np0
+    kappaB  = 4.878171165833662*1.6129 # non-dimensional thermal conductivity of background specie
+                                # (2/3)*tau/L**2*Kb/np0/kB,
+                                # where Kb is the thermal conductivity of background specie
+
+    params.beta = np.array([[0,1,1,1,0,0,0,0,1,1,1,2,1,2,1,1,2,1,1,1,1,1,0,0,1,1,1,1,0,0,1,1,2,1],                     # E
+                            [0,1,1,1,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0],                     # AR+
+                            [0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,1,0,0,0,0,0],                     # AR(m)
+                            [0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0],                     # AR(r)
+                            [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0],                     # AR(4p)
+                            [2,1,1,1,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1]], dtype=np.int64)    # AR
+
+    params.alfa = np.array([[0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,0,0,0,0,1,1],                     # E
+                            [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0],                     # AR+
+                            [2,1,0,2,1,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],                     # AR(m)
+                            [0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,1,0],                     # AR(r)
+                            [0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,0,1],                     # AR(4p)
+                            [0,0,0,0,1,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0]], dtype=np.int64)    # AR
+    
+    # Rxn1:  2AR(m)         ->   2AR
+    # Rxn2:  AR(m) + AR(r)  ->   E + AR+ + AR
+    # Rxn3:  2AR(4p)        ->   E + AR+ + AR
+    # Rxn4:  2AR(m)         ->   E + AR+ + AR
+    # Rxn5:  AR(m) + AR     ->   2AR
+    # Rxn6:  AR(r)          ->   AR + hv
+    # Rxn7:  AR(4p)         ->   AR(m) + hv
+    # Rxn8:  AR(4p)         ->   AR(r) + hv
+    # Rxn9:  E + AR         ->   E + AR(m)
+    # Rxn10: E + AR         ->   E + AR(r)
+    # Rxn11: E + AR         ->   E + AR(4p)
+    # Rxn12: E + AR         ->   2E + AR+
+    # Rxn13: E + AR(m)      ->   E + AR
+    # Rxn14: E + AR(m)      ->   2E + AR+
+    # Rxn15: E + AR(m)      ->   E + AR(r)
+    # Rxn16: E + AR(m)      ->   E + AR(4p)
+    # Rxn17: E + AR(4p)     ->   2E + AR+
+    # Rxn18: E + AR(4p)     ->   E + AR(r)
+    # Rxn19: E + AR(4p)     ->   E + AR(m)
+    # Rxn20: E + AR(r)      ->   E + AR
+    # Rxn21: E + AR(r)      ->   E + AR(m)
+    # Rxn22: E + AR(r)      ->   E + AR(4p)
+    # Rxn23: E + AR+        ->   AR(m) + hv
+    # Rxn24: E + AR+        ->   AR(4p) + hv
+    # Rxn25: 2E + AR+       ->   E + AR(m)
+    # Rxn26: 2E + AR+       ->   E + AR(r)
+    # Rxn27: 2E + AR+       ->   E + AR(4p)
+    # Rxn28: 2E + AR+       ->   E + AR
+    # Rxn29: AR + AR(4p)    ->   AR + AR(m)
+    # Rxn30: AR + AR(4p)    ->   AR + AR(r)
+    # Rxn31: AR(m) + AR(4p) ->   E + AR+ + AR
+    # Rxn32: AR(r) + AR(4p) ->   E + AR+ + AR
+    # Rxn33: E + AR(r)      ->   2E + AR+
+    # Rxn34: E + AR(4p)     ->   E + AR 
+
+    rxnNameDict = {0: "Ar(m) + Ar(m) => Ar + Ar",
+                   1: "Ar(m) + Ar(r) => E + Ar + Ar+",
+                   2: "Ar(4p) + Ar(4p) => E + Ar + Ar+",
+                   3: "Ar(m) + Ar(m) => E + Ar + Ar+",
+                   4: "Ar + Ar(m) => Ar + Ar",
+                   5: "Ar(r) => Ar",
+                   6: "Ar(4p) => Ar(m)",
+                   7: "Ar(4p) => Ar(r)",
+                   8: "Excitation_Metastable",
+                   9: "Excitation_Resonant",
+                   10: "Excitation_4p",
+                   11: "Ionization",
+                   12: "DeExcitation_Metastable",
+                   13: "StepIonization_Metastable",
+                   14: "E + Ar(m) => E + Ar(r)",
+                   15: "E + Ar(m) => E + Ar(4p)",
+                   16: "StepIonization_4p",
+                   17: "E + Ar(4p) => E + Ar(r)",
+                   18: "E + Ar(4p) => E + Ar(m)",
+                   19: "DeExcitation_Resonant",
+                   20: "E + Ar(r) => E + Ar(m)",
+                   21: "E + Ar(r) => E + Ar(4p)",
+                   22: "E + Ar+ => Ar(m)",
+                   23: "E + Ar+ => Ar(4p)",
+                   24: "3BdyRecomb_Metastable",
+                   25: "3BdyRecomb_Resonant",
+                   26: "3BdyRecomb_4p",
+                   27: "3BdyRecomb_Ground",
+                   28: "Ar + Ar(4p) => Ar + Ar(m)",
+                   29: "Ar + Ar(4p) => Ar + Ar(r)",
+                   30: "Ar(m) + Ar(4p) => E + Ar+ + Ar",
+                   31: "Ar(r) + Ar(4p) => E + Ar+ + Ar",
+                   32: "StepIonization_Resonant",
+                   33: "DeExcitation_4p"}
+
+    # 4) Set values in params class
+    params.D[0]    = De
+    params.D[1]    = Di
+    params.D[2]    = Dm
+
+    params.mu[0]   = mue
+    params.mu[1]   = mui
+    params.mu[2]   = mum
+
+    params.A[:]    = Ck[:]
+    params.B[:]    = B[:]
+    params.C[:]    = A[:]
+
+    # Account for the 2/3 term to convert from electron temperature to electron energy
+    for i in range(len(params.A)):
+        params.A[i] *= (2/3)**(params.B[i])
+
+    params.dH[:]   = dH[:]
+    params.dEps[:] = dEps[:]
+    params.qStar   = qStar
+    params.alpha   = alpha
+    params.ks      = ks
+    params.gam     = gam
+    params.kappaB  = kappaB
+    params.nAronp0 = nAr / np0
+    params.p0      = p0
+    params.Tg0     = Tg0
+    params.EC      = 2.0 * me / mAr \
+        * np.sqrt(16.0 * (me + mAr) * e0 * c**2
+                  / (3.0 * np.pi * me * mAr)) * se * nAr * tau
+    # params.EC = 2.0 * me / mAr * 3.8e9 * tau
+
+    params.verticalShift = verticalShift / V0
+
+    # Parameters needed to compute the current with dimensions
+    params.V0Ltau  = V0 / (L * tau)
+    params.V0L     = V0 / L
+    params.LLV0tau = (L*L) / (V0*tau)
+    params.tauL    = L / tau
+    params.np0     = np0           # "nominal" electron density [1/m^3]
+    params.qe      = qe            # unit charge [C]
+    params.eps0    = eps0          # unit charge [C]
+    params.eArea   = electrodeArea # electrode area [m^2]
+
+
+    reactionExpressionTypelist =  np.array([False,False,False,False,False,False,False,False,
+                                           True,True,True,True,True,True,True,True,True,True,True,True,True,True,
+                                           False,False,True,True,True,True,False,False,False,False,True,True])
+
+    thresholded_rxn = np.array([False, False, False, False, False, False, False, False,
+                                True, True, True, True, False, True, True, True, True, False, False, False, False,
+                                True, False, False, False, False, False, False, False, False, False, False, True,
+                                False])
+
+    reactionsList = []
+    LOGFilename = 'interpolationSample%s.log'%str(iSample)
+    f = open(LOGFilename, 'w')
+
+    for i in range(Nr):
+        sample_root_dir = "../../../BOLSIGChemistry_6SpeciesRates"
+        if reactionExpressionTypelist[i]:
+            if i < 14 or i == 16 or i == 19 or i > 23:
+                fileString = sample_root_dir + "/" + rxnNameDict[i]
+                fileName = "%s.%08d.h5" % (fileString, iSample)
+                f = h5.File(fileName, 'r')
+                dataset = f["table"]
+            else:
+                fileString = sample_root_dir + "/" + "StepwiseExcitations"
+                fileName = "%s.%08d.h5" % (fileString, iSample)
+                f = h5.File(fileName, 'r')
+                dataset = f[rxnNameDict[i]]
+            
+            rateCoeff = dataset[:,1]
+            if i > 23 and i < 28:
+                rateCoeff /= 6.022e23**2
+            else:
+                rateCoeff /= 6.022e23
+            Te = dataset[:,0]
+            Te /= 11604.
+
+            ## Removing BOLSIG failures
+            fail_inds = []
+            for j in range(len(rateCoeff)):
+                if rateCoeff[j] == 0.0 and j > np.nonzero(rateCoeff)[0][0]:
+                    fail_inds.append(j)
+
+            Te = np.delete(Te, fail_inds)
+            rateCoeff = np.delete(rateCoeff, fail_inds)
+
+            # Sorting mean energy array and rate coefficient array based on
+            # the mean energy array.
+            Teinds = Te.argsort()
+            rateCoeff = rateCoeff[Teinds]
+            Te = Te[Teinds]
+
+            # Find duplicates
+            TeDuplicateinds = np.where(np.abs(np.diff(Te, axis=0)) > 0.0)
+            TeDuplicateindsForLog = np.where(np.abs(np.diff(Te, axis=0)) == 0.0)
+            rateCoeff = rateCoeff[TeDuplicateinds]
+            Te = Te[TeDuplicateinds]
+
+            # Nondimensionalization of mean energy.
+            Te *= 1.5
+
+            # Find first non-zero value of the coefficient rate.
+            I = np.nonzero(rateCoeff)
+
+            diffRateCoeff = [j-i for i, j in zip(rateCoeff[:-1], rateCoeff[1:])]
+            diffTe = [j-i for i, j in zip(Te[:-1], Te[1:])]
+
+            Monotonicity = np.asarray([j/i for i, j in zip(diffTe, diffRateCoeff)])
+            Monotonicity = np.insert(Monotonicity, 0, 0.0, axis=0)
+
+            Nan = np.isnan(Monotonicity)
+            Inf = np.isinf(Monotonicity)
+            if thresholded_rxn[i] == True:
+                indexPositive = np.where(Monotonicity>0.0)
+            else:
+                indexPositive = np.where(Monotonicity<0.0)
+            Positive = np.full(Monotonicity.shape, False, dtype=bool)
+            Positive[indexPositive] = True
+
+            indices = Nan + Inf + Positive
+
+            #lastFalse = np.where(indices==False)[-1][-1] + 2
+            for k in range(len(Te)):
+                if (Te[k] < 4.5 and indices[k] == False):
+                    lastFalse = k + 2
+
+            # Transformation to log scale.
+            TeLog = np.log(Te)
+
+            # Compute the slope of the rate coefficient between its first two non-zero values.
+            # Finite differences are used.
+            dydx = (rateCoeff[lastFalse + 1] - rateCoeff[lastFalse]) \
+                 / (Te[lastFalse + 1] - Te[lastFalse])
+
+            # Arrhenius form: kf = A * exp(-C / Te)
+            C = Te[lastFalse]**2.0*dydx / rateCoeff[lastFalse]
+
+            # Compute pre-exponential coefficient, A, in log scale.
+            ALog = np.log(rateCoeff[lastFalse]) + C / Te[lastFalse]
+
+            # Transform rate coefficient in log scale.
+            rateCoeffLog = np.zeros(rateCoeff.shape)
+            rateCoeffLog[lastFalse:] = np.log(rateCoeff[lastFalse:])
+            # For the troublesome values, we use the Arrhenius form.
+            rateCoeffLog[0:lastFalse] = ALog - C / Te[0:lastFalse]
+            # Nondimensionalization in log scale.
+            if i < 12:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
+            elif i > 23 and i < 28:
+                rateCoeffLog += - np.log(1.0/tau) + 2*np.log(np0)
+            else:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(np0)
+
+            # Interpolation in log scale.
+            reactionExpressionsLog = CubicSpline(TeLog, rateCoeffLog)
+            # Gradient in log scale
+            reactionTExpressionsLog = CubicSpline.derivative(reactionExpressionsLog)
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf_log = reactionExpressionsLog,
+                                kf_T_log = reactionTExpressionsLog)
+            reactionsList.append(reaction)
+
+            logging.basicConfig(filename=LOGFilename)
+            logging.warning('Interpolation info (Reaction %s):', i)
+            logging.warning('First non-zero entry in the rate coefficient: %s', I[0][0])
+            logging.warning('Monotonicity of the rate coefficient start from entry: %s', lastFalse)
+            logging.warning('Position of possible duplicates in mean energy array: %s',
+                          TeDuplicateindsForLog[0])
+
+        else:
+            fileName = "%s/Arrhenius.%08d.h5" % (sample_root_dir, iSample)
+            f = h5.File(fileName, 'r')
+            arrh_Coeffs = f[rxnNameDict[i]][...]
+            A, B, C = arrh_Coeffs
+
+            # Non-dimensionalize the coefficients
+            if i < 4:
+                A /= 6.022e23
+                A *= tau*np0
+            elif i == 4:
+                A /= 6.022e23
+                A *= tau*nAr
+            elif i > 4 and i < 8:
+                A *= tau
+            elif i > 27 and i < 30:
+                A /= 6.022e23
+                A *= tau*nAr
+            else:
+                A /= 6.022e23
+                A *= tau*np0
+
+            A *= ((2./3.)*11604)**B 
+            C *= 1.5/(e0*11604)
+
+            rxn = eval("lambda energy :" + f"{A} * energy**{B} * np.exp(-{C} / energy)")
+            rxn_T = eval("lambda energy :" + f"{A} * energy**({B}-1) * np.exp(-{C} / energy) * ({B} + {C} / energy)")
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf = rxn, kf_T = rxn_T)
+            reactionsList.append(reaction)
+
+    params.reactionsList = reactionsList
+
+    diffList = []
+    # Data from BOLSIG
+    # Te in [eV]
+    # De * N in [1/(m*s)]
+    transport = h5.File("../../../BOLSIGChemistry_6SpeciesRates/Transport.%08d.h5" % (iSample), 'r')
+    NDe_v_Te = transport["diffusivity"]
+    Te_trans = NDe_v_Te[:,0]
+    Te_trans /= 11604
+    De_interp = (NDe_v_Te[:,1]/nAr)*tau/(L*L)
+    De_spline = CubicSpline(Te_trans, De_interp)
+    De_Te_spline = CubicSpline.derivative(De_spline)
+    diffusivity = Diffusivity(interpolate = True, D_expression = De_spline, D_T_expression = De_Te_spline)
+    diffList.append(diffusivity)
+
+    Ns = 6
+    for i in range(1, Ns):
+        diffList.append(Diffusivity(interpolate = False))
+
+    params.diffusivityList = diffList
+
+    muList = []
+    # Data from BOLSIG
+    # Te in [eV]
+    # Mue * N in [1/(V*m*s)]
+    Nmue_v_Te = transport["mobility"]
+    mue_interp = (Nmue_v_Te[:,1]/nAr)*V0*tau/(L*L)
+    mue_spline = CubicSpline(Te_trans, mue_interp)
+    mue_Te_spline = CubicSpline.derivative(mue_spline)
+    mobility = Mobility(interpolate = True, mu_expression = mue_spline, mu_T_expression = mue_Te_spline)
+    muList.append(mobility)
+
+    Ns = 6
+    for i in range(1, Ns):
+        muList.append(Mobility(interpolate = False))
+
+    params.mobilityList = muList
+
+    # 5) Dump to screen
+    params.print()
diff --git a/psaapProperties_6Species_Sampling_250mTorr.py b/psaapProperties_6Species_Sampling_250mTorr.py
new file mode 100755
index 000000000..ab608c9de
--- /dev/null
+++ b/psaapProperties_6Species_Sampling_250mTorr.py
@@ -0,0 +1,499 @@
+import numpy as np
+from scipy.interpolate import CubicSpline
+import csv
+import matplotlib.pyplot as plt
+import matplotlib.colors as mcolors
+import h5py as h5
+import logging
+
+class Reaction(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Diffusivity(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Mobility(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+
+def setPsaapProperties_6Species_Sampling_250mTorr(gam, inputV0, inputVDC, params, Nr, iSample):
+    """Sets non-dimensional properties corresponding to Liu 2014 paper.
+
+    Inputs:
+      gam : Secondary electron emission coefficient
+      params : chebSolver.modelParams class
+
+    Outputs: None
+      params data is overwritten using values from Liu 2014.
+    """
+    ###################################################################
+    # User specified parameters (you may change these if you wish to
+    # run a different scenario from Liu 2014)
+    ###################################################################
+
+    # densities
+    nAr = 8.05e21     # background number density of Ar [1/m^3] (corresponds to p=100 mTorr)
+    np0 = 8e16        # "nominal" electron density [1/m^3]
+
+    # masses
+    # me = 9.10938356e-31        # mass of an electron [kg]
+    # me = 5.489e-4              # mass of an electron [u]
+    me = 0.511e6                 # mass of an electron [eV/c2]
+    # mAr = 39.948               # mass of an argon atom [u]
+    # mAr = 39.948 * 1.66054e-27 # mass of an argon atom [kg]
+    mAr = 37.2158e9              # mass of an electron [eV/c2]
+    # u = 931.4941e6             # eV/c2
+    c = 299792458                # speed of light [m/s]
+    se = 40                      # momentum cross section [A^2]
+
+    # nominal electron energy
+    e0 = 1.0  # [eV]
+
+    # pressure
+    p  = 33.33*1.5      # [J/m^3] *1.5 to convert it to energy (1 Torr)
+
+    # gas energy at the wall
+    Tg0 = 0.038778    # 3/2*300K*kB ~ (p0 - nT[:,0])/ntot
+
+    # characteristics of driving voltage
+    V0  = inputV0                 # amplitude of driving voltage [V]
+    verticalShift = inputVDC      # DC voltage (vertical shift in driving voltage)
+    tau = (1./13.56e6)             # period of driving voltage [s]
+    L   = 2.00*0.005              # half-gap-width [m] (gap width is 2 cm)
+    electrodeArea = np.pi*0.05**2 # electrode area [m^2] (electrode diameter = 0.1 m)
+
+    # transport parameters
+    nmue = 9.66e21   # argon number density times electron mobility [1/(V*cm*s)]
+    nmum = 0.0
+    nmui = 8.0e19
+    nDe  = 3.86e22   # argon number density times electron diffusivity [1/(cm*s)]
+    nDi  = 2.07e18   # argon number density times ion diffusivity [1/(cm*s)]
+    nDm  = 2.42e18   # argon number density times metastable diffusivity [1/(cm*s)]
+
+    # reaction parameters (NB: k_i = Ck*Ee^B*exp(-A/Ee))
+    #                          Ee = 3/2*Te (Te in eV)
+    #                          -> k_i = [Ck*(2/3)^B] * Ee^B * exp[-(3/2)*A/Ee]
+    # nominal
+    Ck = np.array([2.0e-7,2.1e-9,5.0e-10,6.4e-10,2.1e-15,1.32e8,0.0,3.0e7,3.0e7,0.0,0.0,0.0,0.0,4.3e-10,0.0,3.7e-8,8.9e-7,1.8e-7,3.0e-7,3.0e-7,4.3e-10,9.1e-7,8.9e-7]) # pre-exponential factors [cm^3/s]
+    B  = np.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.74,0.0,0.0,0.51,0.61,0.51,0.51,0.74,0.0,0.51])
+    A  = np.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.59,2.61,0.0,0.0,0.0,0.0,1.59]) # activation temperature [eV]
+    dH = np.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.548,11.624,12.907,15.76,-11.548,4.212,0.076,1.359,2.853,-1.283,-1.359,-11.624,-0.076,0.983]) # energy lost per electron due to ionization rxn [eV]
+    dEps = np.array([0.0,15.76,11.548,11.624,12.907,0.0])
+
+    # BC parameters
+    # ks = 1.19e7  # electron recombination rate [cm/s]
+    ks = 1.366109824889323e7 # electron recombination rate [cm/s/eV]
+
+    ###################################################################
+    # Constants of nature (probably shouldn't change unless you have
+    # root privileges on universe)
+    ###################################################################
+    qe   = 1.6e-19   # unit charge [C]
+    eps0 = 8.86e-12  # permittivity of free space [F/m]
+    kB   = 1.38e-23  # Boltzmann constant [J/K]
+    # kB   = 8.62e−5 # Boltzmann constant [eV/K]
+
+
+    ###################################################################
+    # Calculate non-dimensional parameters
+    ###################################################################
+
+    # 1) Convert input units to base SI (except eV)
+    nDe  *= 100. # 1/(m*s)
+    nDi  *= 100. # 1/(m*s)
+    nDm  *= 100.
+    nmue *= 100. # 1/(V*m*s)
+    nmui *= 100. # 1/(V*m*s)
+    Ck[0:5]  *= 1e-6 # m^3/s
+    Ck[5:9]  *= 1 # 1/s
+    Ck[9:]   *= 1e-6 # m^6/s
+    ks   *= 0.01 # m/s
+    se   *= 1.0e-20  # m^2
+
+    # 2) Compute "raw" transport parameters
+    De  = nDe/nAr
+    Di  = nDi/nAr
+    Dm  = nDm/nAr
+
+    mue = nmue/nAr
+    mui = nmui/nAr
+    mum = nmum/nAr
+
+    # 3) Compute non-dimensional properties required by solver
+    De    = De*tau/(L*L)
+    Di    = Di*tau/(L*L)
+    Dm    = Dm*tau/(L*L)
+
+    mue   = mue*V0*tau/(L*L)
+    mui   = mui*V0*tau/(L*L)
+    mum   = mum*V0*tau/(L*L)
+
+    Ck[0:4]  *= tau*np0
+    Ck[4]    *= tau*nAr
+    Ck[5:9]  *= tau
+    Ck[9:13] *= tau*nAr
+    Ck[13:]  *= tau*np0
+    A       = A*1.5/e0  # 1.5 to convert from temperature to energy
+    dH      = dH/e0
+    qStar   = V0/e0 # qe*V0/e0, since e0 in eV, need qe*V0 in eV, which is just V0 in V
+    alpha   = qe*np0*L*L/(V0*eps0)
+    ks      = ks*tau/L
+    p0      = p/qe/np0
+    kappaB  = 4.878171165833662*1.6129 # non-dimensional thermal conductivity of background specie
+                                # (2/3)*tau/L**2*Kb/np0/kB,
+                                # where Kb is the thermal conductivity of background specie
+
+    params.beta = np.array([[0,1,1,1,0,0,0,0,0,1,1,1,2,1,2,1,1,2,1,1,1,1,1],                     # E
+                            [0,1,1,1,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0],                     # AR+
+                            [0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0],                     # AR(m)
+                            [0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0],                     # AR(r)
+                            [0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1],                     # AR(4p)
+                            [2,1,1,1,2,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0]], dtype=np.int64)    # AR
+
+    params.alfa = np.array([[0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1],                     # E
+                            [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],                     # AR+
+                            [2,1,0,2,1,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0],                     # AR(m)
+                            [0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1],                     # AR(r)
+                            [0,0,2,0,0,0,1,1,1,0,0,0,0,0,0,0,0,1,1,1,0,0,0],                     # AR(4p)
+                            [0,0,0,0,1,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0]], dtype=np.int64)    # AR
+            # Rxn1:  2AR(m)         ->   2AR
+            # Rxn2:  AR(m) + AR(r)  ->   E + AR+ + AR
+            # Rxn3:  2AR(4p)        ->   E + AR+ + AR
+            # Rxn4:  2AR(m)         ->   E + AR+ + AR
+            # Rxn5:  AR(m) + AR     ->   2AR
+            # Rxn6:  AR(r)          ->   AR
+            # Rxn7:  AR(4p)         ->   AR
+            # Rxn8:  AR(4p)         ->   AR(m)
+            # Rxn9:  AR(4p)         ->   AR(r)
+            # Rxn10: E + AR         ->   E + AR(m)
+            # Rxn11: E + AR         ->   E + AR(r)
+            # Rxn12: E + AR         ->   E + AR(4p)
+            # Rxn13: E + AR         ->   2E + AR+
+            # Rxn14: E + AR(m)      ->   E + AR
+            # Rxn15: E + AR(m)      ->   2E + AR+
+            # Rxn16: E + AR(m)      ->   E + AR(r)
+            # Rxn17: E + AR(m)      ->   E + AR(4p)
+            # Rxn18: E + AR(4p)     ->   2E + AR+
+            # Rxn19: E + AR(4p)     ->   E + AR(r)
+            # Rxn20: E + AR(4p)     ->   E + AR(m)
+            # Rxn21: E + AR(r)      ->   E + AR
+            # Rxn22: E + AR(r)      ->   E + AR(m)
+            # Rxn23: E + AR(r)      ->   E + AR(4p)
+
+    rxnNameDict = {0: "Ar(m) + Ar(m) => Ar + Ar",
+                   1: "Ar(m) + Ar(r) => E + Ar + Ar+",
+                   2: "Ar(4p) + Ar(4p) => E + Ar + Ar+",
+                   3: "Ar(m) + Ar(m) => E + Ar + Ar+",
+                   4: "Ar + Ar(m) => Ar + Ar",
+                   5: "Ar(r) => Ar",
+                   6: "Ar(4p) => Ar",
+                   7: "Ar(4p) => Ar(m)",
+                   8: "Ar(4p) => Ar(r)",
+                   9: "1s-metastable",
+                   10: "1s-resonance",
+                   11: "2p-lumped",
+                   12: "Ionization",
+                   13: "E + Ar(m) => E + Ar",
+                   14: "StepIonization",
+                   15: "E + Ar(m) => E + Ar(r)",
+                   16: "E + Ar(m) => E + Ar(4p)",
+                   17: "E + Ar(4p) => E + E + Ar+",
+                   18: "E + Ar(4p) => E + Ar(r)",
+                   19: "E + Ar(4p) => E + Ar(m)",
+                   20: "E + Ar(r) => E + Ar",
+                   21: "E + Ar(r) => E + Ar(m)",
+                   22: "E + Ar(r) => E + Ar(4p)"}
+
+    # 4) Set values in params class
+    params.D[0]    = De
+    params.D[1]    = Di
+    params.D[2]    = Dm
+
+    params.mu[0]   = mue
+    params.mu[1]   = mui
+    params.mu[2]   = mum
+
+    params.A[:]    = Ck[:]
+    params.B[:]    = B[:]
+    params.C[:]    = A[:]
+
+    # Account for the 2/3 term to convert from electron temperature to electron energy
+    for i in range(len(params.A)):
+        params.A[i] *= (2/3)**(params.B[i])
+
+    params.dH[:]   = dH[:]
+    params.dEps[:] = dEps[:]
+    params.qStar   = qStar
+    params.alpha   = alpha
+    params.ks      = ks
+    params.gam     = gam
+    params.kappaB  = kappaB
+    params.nAronp0 = nAr / np0
+    params.p0      = p0
+    params.Tg0     = Tg0
+    params.EC      = 2.0 * me / mAr \
+        * np.sqrt(16.0 * (me + mAr) * e0 * c**2
+                  / (3.0 * np.pi * me * mAr)) * se * nAr * tau
+    # params.EC = 2.0 * me / mAr * 3.8e9 * tau
+
+    params.verticalShift = verticalShift / V0
+
+    # Parameters needed to compute the current with dimensions
+    params.V0Ltau  = V0 / (L * tau)
+    params.V0L     = V0 / L
+    params.LLV0tau = (L*L) / (V0*tau)
+    params.tauL    = L / tau
+    params.np0     = np0           # "nominal" electron density [1/m^3]
+    params.qe      = qe            # unit charge [C]
+    params.eps0    = eps0          # unit charge [C]
+    params.eArea   = electrodeArea # electrode area [m^2]
+
+
+    reactionExpressionTypelist =  np.array([False,False,False,False,False,False,False,False,False,
+                                           True,True,True,True,False,True,True,True,False,True,True,False,True,True])
+
+    reactionsList = []
+    LOGFilename = 'interpolationSample%s.log'%str(iSample)
+    f = open(LOGFilename, 'w')
+
+    for i in range(Nr):
+        sample_root_dir = "../../BOLSIGChemistry_6SpeciesRates"
+        if reactionExpressionTypelist[i]:
+            if (i < 15):
+                fileString = sample_root_dir + "/" + rxnNameDict[i]
+                fileName = "%s.%08d.h5" % (fileString, iSample)
+                f = h5.File(fileName, 'r')
+                dataset = f["table"]
+            else:
+                fileString = sample_root_dir + "/" + "StepwiseExcitations"
+                fileName = "%s.%08d.h5" % (fileString, iSample)
+                f = h5.File(fileName, 'r')
+                dataset = f[rxnNameDict[i]]
+            
+            rateCoeff = dataset[:,1]
+            rateCoeff /= 6.022e23
+            Te = dataset[:,0]
+            Te /= 11604.
+
+            # Sorting mean energy array and rate coefficient array based on
+            # the mean energy array.
+            Teinds = Te.argsort()
+            rateCoeff = rateCoeff[Teinds]
+            Te = Te[Teinds]
+
+            # Find duplicates
+            TeDuplicateinds = np.where(np.abs(np.diff(Te, axis=0)) > 0.0)
+            TeDuplicateindsForLog = np.where(np.abs(np.diff(Te, axis=0)) == 0.0)
+            rateCoeff = rateCoeff[TeDuplicateinds]
+            Te = Te[TeDuplicateinds]
+
+            # Nondimensionalization of mean energy.
+            Te *= 1.5
+
+            # Find first non-zero value of the coefficient rate.
+            I = np.nonzero(rateCoeff)
+
+            diffRateCoeff = [j-i for i, j in zip(rateCoeff[:-1], rateCoeff[1:])]
+            diffTe = [j-i for i, j in zip(Te[:-1], Te[1:])]
+
+            Monotonicity = np.asarray([j/i for i, j in zip(diffTe, diffRateCoeff)])
+            Monotonicity = np.insert(Monotonicity, 0, 0.0, axis=0)
+
+            Nan = np.isnan(Monotonicity)
+            Inf = np.isinf(Monotonicity)
+            indexPositive = np.where(Monotonicity>0.0)
+            Positive = np.full(Monotonicity.shape, False, dtype=bool)
+            Positive[indexPositive] = True
+
+            indices = Nan + Inf + Positive
+
+            #lastFalse = np.where(indices==False)[-1][-1] + 2
+            for k in range(len(Te)):
+                if (Te[k] < 4.5 and indices[k] == False):
+                    lastFalse = k + 2
+
+            # Transformation to log scale.
+            TeLog = np.log(Te)
+
+            # Compute the slope of the rate coefficient between its first two non-zero values.
+            # Finite differences are used.
+            dydx = (rateCoeff[lastFalse + 1] - rateCoeff[lastFalse]) \
+                 / (Te[lastFalse + 1] - Te[lastFalse])
+
+            # Arrhenius form: kf = A * exp(-C / Te)
+            C = Te[lastFalse]**2.0*dydx / rateCoeff[lastFalse]
+
+            # Compute pre-exponential coefficient, A, in log scale.
+            ALog = np.log(rateCoeff[lastFalse]) + C / Te[lastFalse]
+
+            # Transform rate coefficient in log scale.
+            rateCoeffLog = np.zeros(rateCoeff.shape)
+            rateCoeffLog[lastFalse:] = np.log(rateCoeff[lastFalse:])
+            # For the troublesome values, we use the Arrhenius form.
+            rateCoeffLog[0:lastFalse] = ALog - C / Te[0:lastFalse]
+            # Nondimensionalization in log scale.
+            if i == 9:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
+            elif i == 10:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
+            elif i == 11:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
+            elif i == 12:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
+            else:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(np0)
+
+            # Interpolation in log scale.
+            reactionExpressionsLog = CubicSpline(TeLog, rateCoeffLog)
+            # Gradient in log scale
+            reactionTExpressionsLog = CubicSpline.derivative(reactionExpressionsLog)
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf_log = reactionExpressionsLog,
+                                kf_T_log = reactionTExpressionsLog)
+            reactionsList.append(reaction)
+
+            logging.basicConfig(filename=LOGFilename)
+            logging.warning('Interpolation info (Reaction %s):', i)
+            logging.warning('First non-zero entry in the rate coefficient: %s', I[0][0])
+            logging.warning('Monotonicity of the rate coefficient start from entry: %s', lastFalse)
+            logging.warning('Position of possible duplicates in mean energy array: %s',
+                          TeDuplicateindsForLog[0])
+
+        else:
+            fileName = "%s/Arrhenius.%08d.h5" % (sample_root_dir, iSample)
+            f = h5.File(fileName, 'r')
+            arrh_Coeffs = f[rxnNameDict[i]][...]
+            A, B, C = arrh_Coeffs
+
+            # Non-dimensionalize the coefficients
+            if (i < 4):
+                A /= 6.022e23
+                A *= tau*np0
+            elif (i == 4):
+                A /= 6.022e23
+                A *= tau*nAr
+            elif (i > 4 and i < 9):
+                A *= tau
+            #elif (i > 8 and i < 13):
+            #    A /= 6.022e23
+            #    A *= tau*nAr
+            else:
+                A /= 6.022e23
+                A *= tau*np0
+
+            A *= ((2./3.)*11604)**B 
+            C *= 1.5/(e0*11604)
+
+            rxn = eval("lambda energy :" + f"{A} * energy**{B} * np.exp(-{C} / energy)")
+            rxn_T = eval("lambda energy :" + f"{A} * energy**({B}-1) * np.exp(-{C} / energy) * ({B} + {C} / energy)")
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf = rxn, kf_T = rxn_T)
+            reactionsList.append(reaction)
+
+    params.reactionsList = reactionsList
+
+    diffList = []
+    # Data from BOLSIG
+    # Te in [eV]
+    # De * N in [1/(m*s)]
+    #NDe_v_Te = np.array([ [-0.05, 1.8e25],         [0.0, 1.8e25],           [0.05, 1.8e25],          [0.1, 1.8e25],
+    #                      [0.2103718, 1.8e25],     [0.2244455, 1.8e25],     [0.2390528, 1.8e25],     [0.2544605, 1.8e25],
+    #                      [0.2710021, 1.8e25],     [0.2886776, 1.8e25],     [0.3078205, 1.8e25],     [0.3286309, 1.8e25],
+    #                      [0.3513089, 1.8e25],     [0.3760546, 1.8e25],     [0.4032682, 1.8e25],     [0.4331498, 1.8e25],
+    #                      [0.4659662, 1.8e25],     [0.5021843, 1.8e25],     [0.5424044, 1.8e25],     [0.5868266, 1.8e25],
+    #                      [0.6359845, 1.8e25],     [0.690345, 1.8e25],      [0.749041, 1.8e25],      [0.813073, 1.8e25],
+    #                      [1.217942, 1.46E+25],    [1.319326, 1.39E+25],    [1.430048, 1.32E+25],    [1.550108, 1.26E+25],
+    #                      [1.681507, 1.20E+25],    [1.823578, 1.15E+25],    [1.977655, 1.09E+25],    [2.143071, 1.05E+25],
+    #                      [2.319826, 9.98E+24],    [2.508587, 9.54E+24],    [2.709354, 9.13E+24],    [2.916124, 8.75E+24],
+    #                      [3.112889, 8.45E+24],    [3.272969, 8.24E+24],    [3.383691, 8.14E+24],    [3.456394, 8.09E+24],
+    #                      [3.505085, 8.07E+24],    [3.544438, 8.05E+24],    [3.579789, 8.03E+24],    [3.616474, 8.01E+24],
+    #                      [3.656494, 7.97E+24],    [3.701183, 7.93E+24],    [3.751875, 7.87E+24],    [3.807903, 7.81E+24],
+    #                      [3.871268, 7.75E+24],    [3.941303, 7.68E+24],    [4.018675, 7.61E+24],    [4.102717, 7.53E+24],
+    #                      [4.193429, 7.5E+24],     [4.290811, 7.5E+24],     [4.39553, 7.5E+24],      [4.507586, 7.5E+24],
+    #                      [5., 7.5E+24],           [6., 7.5E+24],           [7., 7.5E+24],           [8., 7.5E+24],
+    #                      [9., 7.5E+24],           [10., 7.5E+24],          [11., 7.5E+24],          [12., 7.5E+24]])
+
+    transport = h5.File("../../BOLSIGChemistry_6SpeciesRates/Transport.%08d.h5" % (iSample), 'r')
+    #Te = NDe_v_Te[:,0]
+    NDe_v_Te = transport["diffusivity"]
+    Te_trans = NDe_v_Te[:,0]
+    Te_trans /= 11604
+    print("Te_min = {0:.6e}".format(NDe_v_Te[0,0]))
+    print("Te_max = {0:.6e}".format(NDe_v_Te[-1,0]))
+    De_interp = (NDe_v_Te[:,1]/nAr)*tau/(L*L)
+    De_spline = CubicSpline(Te_trans, De_interp)
+    De_Te_spline = CubicSpline.derivative(De_spline)
+    diffusivity = Diffusivity(interpolate = True, D_expression = De_spline, D_T_expression = De_Te_spline)
+    diffList.append(diffusivity)
+
+    Ns = 6
+    for i in range(1, Ns):
+        diffList.append(Diffusivity(interpolate = False))
+
+    params.diffusivityList = diffList
+
+    muList = []
+    # Data from BOLSIG
+    # Te in [eV]
+    # Mue * N in [1/(V*m*s)]
+    #Nmue_v_Te = np.array([[0.02846756, 1.289e+26], [0.02975487, 1.33e+26],   [0.03173586, 1.383e+26],  [0.03477738,1.448e+26],
+    #                      [0.03937968, 1.523e+26], [0.04614973, 1.604e+26],  [0.05561446, 1.679e+26],  [0.0681674,1.735e+26],
+    #                      [0.0835084, 1.749e+26],  [0.1008504, 1.718e+26],   [0.1187927, 1.639e+26],   [0.1363348,1.522e+26],
+    #                      [0.1528764, 1.386e+26],  [0.1682841, 1.245e+26],   [0.1826913, 1.108e+26],   [0.1965649,9.809e+25],
+    #                      [0.2103718, 8.662e+25],  [0.2244455, 7.641e+25],   [0.2390528, 6.74e+25],    [0.2544605,5.947e+25],
+    #                      [0.2710021, 5.249e+25],  [0.2886776, 4.636e+25],   [0.3078205, 4.097e+25],   [0.3286309,3.623e+25],
+    #                      [0.3513089, 3.206e+25],  [0.3760546, 2.839e+25],   [0.4032682, 2.517e+25],   [0.4331498,2.232e+25],
+    #                      [0.4659662, 1.981e+25],  [0.5021843, 1.76e+25],    [0.5424044, 1.565e+25],   [0.5868266,1.392e+25],
+    #                      [0.6359845, 1.239e+25],  [0.690345, 1.103e+25],    [0.749041,  9.8e+24],     [0.813073,8.699e+24],
+    #                      [0.882441, 7.711e+24],   [0.957145, 6.828e+24],    [1.037185,  6.04e+24],    [1.123895,5.344e+24],
+    #                      [1.217942, 4.729e+24],   [1.319326, 4.186e+24],    [1.430048,  3.707e+24],   [1.550108,3.283e+24],
+    #                      [1.681507, 2.907e+24],   [1.823578, 2.574e+24],    [1.977655,  2.278e+24],   [2.143071,2.015e+24],
+    #                      [2.319826, 1.779e+24],   [2.508587, 1.569e+24],    [2.709354,  1.384e+24],   [2.916124,1.228e+24],
+    #                      [3.112889, 1.115e+24],   [3.272969, 1.055e+24],    [3.383691,  1.038e+24],   [3.456394,1.044e+24],
+    #                      [3.505085, 1.054e+24],   [3.544438, 1.062e+24],    [3.579789,  1.064e+24],   [3.616474, 1.059e+24],
+    #                      [3.656494, 1.048e+24],   [3.701183, 1.033e+24],    [3.751875,  1.014e+24],   [3.807903, 9.928e+23],
+    #                      [3.871268, 9.697e+23],   [3.941303,  9.459e+23],   [4.018675, 9.22e+23],     [4.102717, 8.991e+23],
+    #                      [4.193429, 8.773e+23],   [4.290811,  8.57e+23],    [4.39553, 8.383e+23],     [4.507586, 8.21e+23],
+    #                      [4.627646, 8.051e+23],   [4.757711,  7.901e+23],   [4.899115, 7.757e+23],    [5.054526, 7.617e+23],
+    #                      [5.227946, 7.479e+23],   [5.423377,  7.339e+23],   [5.646822, 7.198e+23],    [5.905618, 7.056e+23],
+    #                      [6.20977, 6.91e+23],     [6.571284,  6.757e+23],   [7.01017, 6.607e+23],     [7.54377, 6.458e+23],
+    #                      [8.19743, 6.303e+23],    [9.01117,   6.155e+23],   [10.02501, 6e+23],        [11.30565, 5.843e+23],
+    #                      [12.89978, 5.677e+23],   [14.91412,  5.51e+23],    [17.41537, 5.326e+23],    [20.57028, 5.149e+23],
+    #                      [24.51225, 4.968e+23],   [29.45472,  4.784e+23],   [35.6845, 4.602e+23],     [43.58845, 4.421e+23],
+    #                      [53.86692, 4.269e+23],   [67.367,    4.134e+23],   [85.4427, 4.026e+23],     [100.0, 4.026e+23]])
+
+    #Te = Nmue_v_Te[:,0]
+    Nmue_v_Te = transport["mobility"]
+    mue_interp = (Nmue_v_Te[:,1]/nAr)*V0*tau/(L*L)
+    mue_spline = CubicSpline(Te_trans, mue_interp)
+    mue_Te_spline = CubicSpline.derivative(mue_spline)
+    mobility = Mobility(interpolate = True, mu_expression = mue_spline, mu_T_expression = mue_Te_spline)
+    muList.append(mobility)
+
+    Ns = 6
+    for i in range(1, Ns):
+        muList.append(Mobility(interpolate = False))
+
+    params.mobilityList = muList
+
+    # 5) Dump to screen
+    params.print()
diff --git a/psaapProperties_6Species_Sampling_250mTorr_Expanded.py b/psaapProperties_6Species_Sampling_250mTorr_Expanded.py
new file mode 100755
index 000000000..710dc7193
--- /dev/null
+++ b/psaapProperties_6Species_Sampling_250mTorr_Expanded.py
@@ -0,0 +1,494 @@
+import numpy as np
+from scipy.interpolate import CubicSpline
+import csv
+import matplotlib.pyplot as plt
+import matplotlib.colors as mcolors
+import h5py as h5
+import logging
+
+class Reaction(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Diffusivity(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Mobility(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+
+def setPsaapProperties_6Species_Sampling_250mTorr_Expanded(gam, inputV0, inputVDC, params, Nr, iSample):
+    """Sets non-dimensional properties corresponding to Liu 2014 paper.
+
+    Inputs:
+      gam : Secondary electron emission coefficient
+      params : chebSolver.modelParams class
+
+    Outputs: None
+      params data is overwritten using values from Liu 2014.
+    """
+    ###################################################################
+    # User specified parameters (you may change these if you wish to
+    # run a different scenario from Liu 2014)
+    ###################################################################
+
+    # densities
+    nAr = 8.05e21     # background number density of Ar [1/m^3] (corresponds to p = 250 mTorr)
+    np0 = 8e16        # "nominal" electron density [1/m^3]
+
+    # masses
+    # me = 9.10938356e-31        # mass of an electron [kg]
+    # me = 5.489e-4              # mass of an electron [u]
+    me = 0.511e6                 # mass of an electron [eV/c2]
+    # mAr = 39.948               # mass of an argon atom [u]
+    # mAr = 39.948 * 1.66054e-27 # mass of an argon atom [kg]
+    mAr = 37.2158e9              # mass of an electron [eV/c2]
+    # u = 931.4941e6             # eV/c2
+    c = 299792458                # speed of light [m/s]
+    se = 40                      # momentum cross section [A^2]
+
+    # nominal electron energy
+    e0 = 1.0  # [eV]
+
+    # pressure
+    p  = 33.3*1.5      # [J/m^3] *1.5 to convert it to energy (250 mTorr)
+
+    # gas energy at the wall
+    Tg0 = 0.038778    # 3/2*300K*kB ~ (p0 - nT[:,0])/ntot
+
+    # characteristics of driving voltage
+    V0  = inputV0                 # amplitude of driving voltage [V]
+    verticalShift = inputVDC      # DC voltage (vertical shift in driving voltage)
+    tau = (1./13.56e6)             # period of driving voltage [s]
+    L   = 2.00*0.005              # half-gap-width [m] (gap width is 2 cm)
+    electrodeArea = np.pi*0.05**2 # electrode area [m^2] (electrode diameter = 0.1 m)
+
+    # Add voltage uncertainty
+    V0 += h5.File('../../../BOLSIGChemistry_Voltage/Voltage.%08d.h5' % (iSample), 'r')["V_Err"][0]
+
+    # transport parameters
+    nmue = 9.66e21   # argon number density times electron mobility [1/(V*cm*s)]
+    nmum = 0.0
+    nmui = 8.0e19
+    nDe  = 3.86e22   # argon number density times electron diffusivity [1/(cm*s)]
+    nDi  = 2.07e18   # argon number density times ion diffusivity [1/(cm*s)]
+    nDm  = 2.42e18   # argon number density times metastable diffusivity [1/(cm*s)]
+
+    # reaction parameters (NB: k_i = Ck*Ee^B*exp(-A/Ee))
+    #                          Ee = 3/2*Te (Te in eV)
+    #                          -> k_i = [Ck*(2/3)^B] * Ee^B * exp[-(3/2)*A/Ee]
+    # nominal
+    Ck = np.array([2.0e-13,2.1e-15,5.0e-16,6.4e-16,2.1e-21,1.32e8,1.72e7,1.50e7,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0e-18,4.0e-19,0.0,0.0,0.0,0.0,2.5e-17,2.5e-17,1.0e-15,1.0e-15,0.0,0.0]) # pre-exponential factors [m^3/s]
+    B  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-0.5,0,0,0,0,0,0,0,0,0,0])
+    A  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]) # activation temperature [eV]
+    dH = np.array([0.0,-7.541,-10.577,-7.393,0.0,0.0,0.0,0.0,11.577,11.725,13.168,15.76,-11.577,4.183,0.148,1.592,2.592,-1.444,-1.592,-11.725,-0.148,1.444,0.0,0.0,-4.183,-4.035,-2.592,-15.76,0.0,0.0,-8.985,-9.133,4.035,-13.168]) # energy lost per electron due to ionization rxn [eV]
+    dEps = np.array([0.0,15.76,11.577,11.725,13.168,0.0])
+
+    # BC parameters
+    # ks = 1.19e7  # electron recombination rate [cm/s]
+    ks = 1.366109824889323e7 # electron recombination rate [cm/s/eV]
+
+    ###################################################################
+    # Constants of nature (probably shouldn't change unless you have
+    # root privileges on universe)
+    ###################################################################
+    qe   = 1.6e-19   # unit charge [C]
+    eps0 = 8.86e-12  # permittivity of free space [F/m]
+    kB   = 1.38e-23  # Boltzmann constant [J/K]
+    # kB   = 8.62e−5 # Boltzmann constant [eV/K]
+
+
+    ###################################################################
+    # Calculate non-dimensional parameters
+    ###################################################################
+
+    # 1) Convert input units to base SI (except eV)
+    nDe  *= 100. # 1/(m*s)
+    nDi  *= 100. # 1/(m*s)
+    nDm  *= 100.
+    nmue *= 100. # 1/(V*m*s)
+    nmui *= 100. # 1/(V*m*s)
+    ks   *= 0.01 # m/s
+    se   *= 1.0e-20  # m^2
+
+    # 2) Compute "raw" transport parameters
+    De  = nDe/nAr
+    Di  = nDi/nAr
+    Dm  = nDm/nAr
+
+    mue = nmue/nAr
+    mui = nmui/nAr
+    mum = nmum/nAr
+
+    # 3) Compute non-dimensional properties required by solver
+    De    = De*tau/(L*L)
+    Di    = Di*tau/(L*L)
+    Dm    = Dm*tau/(L*L)
+
+    mue   = mue*V0*tau/(L*L)
+    mui   = mui*V0*tau/(L*L)
+    mum   = mum*V0*tau/(L*L)
+
+    Ck[0:4]  *= tau*np0
+    Ck[4]    *= tau*nAr
+    Ck[5:8]  *= tau
+    Ck[8:12] *= tau*nAr
+    Ck[12:24]  *= tau*np0
+    Ck[24:28] *= tau*np0*np0
+    Ck[28:30] *= tau*nAr
+    Ck[30:] *= tau*np0
+    A       = A*1.5/e0  # 1.5 to convert from temperature to energy
+    dH      = dH/e0
+    qStar   = V0/e0 # qe*V0/e0, since e0 in eV, need qe*V0 in eV, which is just V0 in V
+    alpha   = qe*np0*L*L/(V0*eps0)
+    ks      = ks*tau/L
+    p0      = p/qe/np0
+    kappaB  = 4.878171165833662*1.6129 # non-dimensional thermal conductivity of background specie
+                                # (2/3)*tau/L**2*Kb/np0/kB,
+                                # where Kb is the thermal conductivity of background specie
+
+    params.beta = np.array([[0,1,1,1,0,0,0,0,1,1,1,2,1,2,1,1,2,1,1,1,1,1,0,0,1,1,1,1,0,0,1,1,2,1],                     # E
+                            [0,1,1,1,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0],                     # AR+
+                            [0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,1,0,0,0,0,0],                     # AR(m)
+                            [0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0],                     # AR(r)
+                            [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0],                     # AR(4p)
+                            [2,1,1,1,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1]], dtype=np.int64)    # AR
+
+    params.alfa = np.array([[0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,0,0,0,0,1,1],                     # E
+                            [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0],                     # AR+
+                            [2,1,0,2,1,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],                     # AR(m)
+                            [0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,1,0],                     # AR(r)
+                            [0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,0,1],                     # AR(4p)
+                            [0,0,0,0,1,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0]], dtype=np.int64)    # AR
+    
+    # Rxn1:  2AR(m)         ->   2AR
+    # Rxn2:  AR(m) + AR(r)  ->   E + AR+ + AR
+    # Rxn3:  2AR(4p)        ->   E + AR+ + AR
+    # Rxn4:  2AR(m)         ->   E + AR+ + AR
+    # Rxn5:  AR(m) + AR     ->   2AR
+    # Rxn6:  AR(r)          ->   AR + hv
+    # Rxn7:  AR(4p)         ->   AR(m) + hv
+    # Rxn8:  AR(4p)         ->   AR(r) + hv
+    # Rxn9:  E + AR         ->   E + AR(m)
+    # Rxn10: E + AR         ->   E + AR(r)
+    # Rxn11: E + AR         ->   E + AR(4p)
+    # Rxn12: E + AR         ->   2E + AR+
+    # Rxn13: E + AR(m)      ->   E + AR
+    # Rxn14: E + AR(m)      ->   2E + AR+
+    # Rxn15: E + AR(m)      ->   E + AR(r)
+    # Rxn16: E + AR(m)      ->   E + AR(4p)
+    # Rxn17: E + AR(4p)     ->   2E + AR+
+    # Rxn18: E + AR(4p)     ->   E + AR(r)
+    # Rxn19: E + AR(4p)     ->   E + AR(m)
+    # Rxn20: E + AR(r)      ->   E + AR
+    # Rxn21: E + AR(r)      ->   E + AR(m)
+    # Rxn22: E + AR(r)      ->   E + AR(4p)
+    # Rxn23: E + AR+        ->   AR(m) + hv
+    # Rxn24: E + AR+        ->   AR(4p) + hv
+    # Rxn25: 2E + AR+       ->   E + AR(m)
+    # Rxn26: 2E + AR+       ->   E + AR(r)
+    # Rxn27: 2E + AR+       ->   E + AR(4p)
+    # Rxn28: 2E + AR+       ->   E + AR
+    # Rxn29: AR + AR(4p)    ->   AR + AR(m)
+    # Rxn30: AR + AR(4p)    ->   AR + AR(r)
+    # Rxn31: AR(m) + AR(4p) ->   E + AR+ + AR
+    # Rxn32: AR(r) + AR(4p) ->   E + AR+ + AR
+    # Rxn33: E + AR(r)      ->   2E + AR+
+    # Rxn34: E + AR(4p)     ->   E + AR 
+
+    rxnNameDict = {0: "Ar(m) + Ar(m) => Ar + Ar",
+                   1: "Ar(m) + Ar(r) => E + Ar + Ar+",
+                   2: "Ar(4p) + Ar(4p) => E + Ar + Ar+",
+                   3: "Ar(m) + Ar(m) => E + Ar + Ar+",
+                   4: "Ar + Ar(m) => Ar + Ar",
+                   5: "Ar(r) => Ar",
+                   6: "Ar(4p) => Ar(m)",
+                   7: "Ar(4p) => Ar(r)",
+                   8: "Excitation_Metastable",
+                   9: "Excitation_Resonant",
+                   10: "Excitation_4p",
+                   11: "Ionization",
+                   12: "DeExcitation_Metastable",
+                   13: "StepIonization_Metastable",
+                   14: "E + Ar(m) => E + Ar(r)",
+                   15: "E + Ar(m) => E + Ar(4p)",
+                   16: "StepIonization_4p",
+                   17: "E + Ar(4p) => E + Ar(r)",
+                   18: "E + Ar(4p) => E + Ar(m)",
+                   19: "DeExcitation_Resonant",
+                   20: "E + Ar(r) => E + Ar(m)",
+                   21: "E + Ar(r) => E + Ar(4p)",
+                   22: "E + Ar+ => Ar(m)",
+                   23: "E + Ar+ => Ar(4p)",
+                   24: "3BdyRecomb_Metastable",
+                   25: "3BdyRecomb_Resonant",
+                   26: "3BdyRecomb_4p",
+                   27: "3BdyRecomb_Ground",
+                   28: "Ar + Ar(4p) => Ar + Ar(m)",
+                   29: "Ar + Ar(4p) => Ar + Ar(r)",
+                   30: "Ar(m) + Ar(4p) => E + Ar+ + Ar",
+                   31: "Ar(r) + Ar(4p) => E + Ar+ + Ar",
+                   32: "StepIonization_Resonant",
+                   33: "DeExcitation_4p"}
+
+    # 4) Set values in params class
+    params.D[0]    = De
+    params.D[1]    = Di
+    params.D[2]    = Dm
+
+    params.mu[0]   = mue
+    params.mu[1]   = mui
+    params.mu[2]   = mum
+
+    params.A[:]    = Ck[:]
+    params.B[:]    = B[:]
+    params.C[:]    = A[:]
+
+    # Account for the 2/3 term to convert from electron temperature to electron energy
+    for i in range(len(params.A)):
+        params.A[i] *= (2/3)**(params.B[i])
+
+    params.dH[:]   = dH[:]
+    params.dEps[:] = dEps[:]
+    params.qStar   = qStar
+    params.alpha   = alpha
+    params.ks      = ks
+    params.gam     = gam
+    params.kappaB  = kappaB
+    params.nAronp0 = nAr / np0
+    params.p0      = p0
+    params.Tg0     = Tg0
+    params.EC      = 2.0 * me / mAr \
+        * np.sqrt(16.0 * (me + mAr) * e0 * c**2
+                  / (3.0 * np.pi * me * mAr)) * se * nAr * tau
+    # params.EC = 2.0 * me / mAr * 3.8e9 * tau
+
+    params.verticalShift = verticalShift / V0
+
+    # Parameters needed to compute the current with dimensions
+    params.V0Ltau  = V0 / (L * tau)
+    params.V0L     = V0 / L
+    params.LLV0tau = (L*L) / (V0*tau)
+    params.tauL    = L / tau
+    params.np0     = np0           # "nominal" electron density [1/m^3]
+    params.qe      = qe            # unit charge [C]
+    params.eps0    = eps0          # unit charge [C]
+    params.eArea   = electrodeArea # electrode area [m^2]
+
+
+    reactionExpressionTypelist =  np.array([False,False,False,False,False,False,False,False,
+                                           True,True,True,True,True,True,True,True,True,True,True,True,True,True,
+                                           False,False,True,True,True,True,False,False,False,False,True,True])
+
+    thresholded_rxn = np.array([False, False, False, False, False, False, False, False,
+                                True, True, True, True, False, True, True, True, True, False, False, False, False,
+                                True, False, False, False, False, False, False, False, False, False, False, True,
+                                False])
+    reactionsList = []
+    LOGFilename = 'interpolationSample%s.log'%str(iSample)
+    f = open(LOGFilename, 'w')
+
+    for i in range(Nr):
+        sample_root_dir = "../../../BOLSIGChemistry_6SpeciesRates"
+        if reactionExpressionTypelist[i]:
+            if i < 14 or i == 16 or i == 19 or i > 23:
+                fileString = sample_root_dir + "/" + rxnNameDict[i]
+                fileName = "%s.%08d.h5" % (fileString, iSample)
+                f = h5.File(fileName, 'r')
+                dataset = f["table"]
+            else:
+                fileString = sample_root_dir + "/" + "StepwiseExcitations"
+                fileName = "%s.%08d.h5" % (fileString, iSample)
+                f = h5.File(fileName, 'r')
+                dataset = f[rxnNameDict[i]]
+            
+            rateCoeff = dataset[:,1]
+            if i > 23 and i < 28:
+                rateCoeff /= 6.022e23**2
+            else:
+                rateCoeff /= 6.022e23
+            Te = dataset[:,0]
+            Te /= 11604.
+
+            ## Removing BOLSIG failures
+            fail_inds = []
+            for j in range(len(rateCoeff)):
+                if rateCoeff[j] == 0.0 and j > np.nonzero(rateCoeff)[0][0]:
+                    fail_inds.append(j)
+
+            Te = np.delete(Te, fail_inds)
+            rateCoeff = np.delete(rateCoeff, fail_inds)
+
+            # Sorting mean energy array and rate coefficient array based on
+            # the mean energy array.
+            Teinds = Te.argsort()
+            rateCoeff = rateCoeff[Teinds]
+            Te = Te[Teinds]
+
+            # Find duplicates
+            TeDuplicateinds = np.where(np.abs(np.diff(Te, axis=0)) > 0.0)
+            TeDuplicateindsForLog = np.where(np.abs(np.diff(Te, axis=0)) == 0.0)
+            rateCoeff = rateCoeff[TeDuplicateinds]
+            Te = Te[TeDuplicateinds]
+
+            # Nondimensionalization of mean energy.
+            Te *= 1.5
+
+            # Find first non-zero value of the coefficient rate.
+            I = np.nonzero(rateCoeff)
+
+            diffRateCoeff = [j-i for i, j in zip(rateCoeff[:-1], rateCoeff[1:])]
+            diffTe = [j-i for i, j in zip(Te[:-1], Te[1:])]
+
+            Monotonicity = np.asarray([j/i for i, j in zip(diffTe, diffRateCoeff)])
+            Monotonicity = np.insert(Monotonicity, 0, 0.0, axis=0)
+
+            Nan = np.isnan(Monotonicity)
+            Inf = np.isinf(Monotonicity)
+            if thresholded_rxn[i] == True:
+                indexPositive = np.where(Monotonicity>0.0)
+            else:
+                indexPositive = np.where(Monotonicity<0.0)
+            Positive = np.full(Monotonicity.shape, False, dtype=bool)
+            Positive[indexPositive] = True
+
+            indices = Nan + Inf + Positive
+
+            #lastFalse = np.where(indices==False)[-1][-1] + 2
+            for k in range(len(Te)):
+                if (Te[k] < 4.5 and indices[k] == False):
+                    lastFalse = k + 2
+
+            # Transformation to log scale.
+            TeLog = np.log(Te)
+
+            # Compute the slope of the rate coefficient between its first two non-zero values.
+            # Finite differences are used.
+            dydx = (rateCoeff[lastFalse + 1] - rateCoeff[lastFalse]) \
+                 / (Te[lastFalse + 1] - Te[lastFalse])
+
+            # Arrhenius form: kf = A * exp(-C / Te)
+            C = Te[lastFalse]**2.0*dydx / rateCoeff[lastFalse]
+
+            # Compute pre-exponential coefficient, A, in log scale.
+            ALog = np.log(rateCoeff[lastFalse]) + C / Te[lastFalse]
+
+            # Transform rate coefficient in log scale.
+            rateCoeffLog = np.zeros(rateCoeff.shape)
+            rateCoeffLog[lastFalse:] = np.log(rateCoeff[lastFalse:])
+            # For the troublesome values, we use the Arrhenius form.
+            rateCoeffLog[0:lastFalse] = ALog - C / Te[0:lastFalse]
+            # Nondimensionalization in log scale.
+            if i < 12:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
+            elif i > 23 and i < 28:
+                rateCoeffLog += - np.log(1.0/tau) + 2*np.log(np0)
+            else:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(np0)
+
+            # Interpolation in log scale.
+            reactionExpressionsLog = CubicSpline(TeLog, rateCoeffLog)
+            # Gradient in log scale
+            reactionTExpressionsLog = CubicSpline.derivative(reactionExpressionsLog)
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf_log = reactionExpressionsLog,
+                                kf_T_log = reactionTExpressionsLog)
+            reactionsList.append(reaction)
+
+            logging.basicConfig(filename=LOGFilename)
+            logging.warning('Interpolation info (Reaction %s):', i)
+            logging.warning('First non-zero entry in the rate coefficient: %s', I[0][0])
+            logging.warning('Monotonicity of the rate coefficient start from entry: %s', lastFalse)
+            logging.warning('Position of possible duplicates in mean energy array: %s',
+                          TeDuplicateindsForLog[0])
+
+        else:
+            fileName = "%s/Arrhenius.%08d.h5" % (sample_root_dir, iSample)
+            f = h5.File(fileName, 'r')
+            arrh_Coeffs = f[rxnNameDict[i]][...]
+            A, B, C = arrh_Coeffs
+
+            # Non-dimensionalize the coefficients
+            if i < 4:
+                A /= 6.022e23
+                A *= tau*np0
+            elif i == 4:
+                A /= 6.022e23
+                A *= tau*nAr
+            elif i > 4 and i < 8:
+                A *= tau
+            elif i > 27 and i < 30:
+                A /= 6.022e23
+                A *= tau*nAr
+            else:
+                A /= 6.022e23
+                A *= tau*np0
+
+            A *= ((2./3.)*11604)**B 
+            C *= 1.5/(e0*11604)
+
+            rxn = eval("lambda energy :" + f"{A} * energy**{B} * np.exp(-{C} / energy)")
+            rxn_T = eval("lambda energy :" + f"{A} * energy**({B}-1) * np.exp(-{C} / energy) * ({B} + {C} / energy)")
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf = rxn, kf_T = rxn_T)
+            reactionsList.append(reaction)
+
+    params.reactionsList = reactionsList
+
+    diffList = []
+    # Data from BOLSIG
+    # Te in [eV]
+    # De * N in [1/(m*s)]
+    transport = h5.File("../../../BOLSIGChemistry_6SpeciesRates/Transport.%08d.h5" % (iSample), 'r')
+    NDe_v_Te = transport["diffusivity"]
+    Te_trans = NDe_v_Te[:,0]
+    Te_trans /= 11604
+    De_interp = (NDe_v_Te[:,1]/nAr)*tau/(L*L)
+    De_spline = CubicSpline(Te_trans, De_interp)
+    De_Te_spline = CubicSpline.derivative(De_spline)
+    diffusivity = Diffusivity(interpolate = True, D_expression = De_spline, D_T_expression = De_Te_spline)
+    diffList.append(diffusivity)
+
+    Ns = 6
+    for i in range(1, Ns):
+        diffList.append(Diffusivity(interpolate = False))
+
+    params.diffusivityList = diffList
+
+    muList = []
+    # Data from BOLSIG
+    # Te in [eV]
+    # Mue * N in [1/(V*m*s)]
+    Nmue_v_Te = transport["mobility"]
+    mue_interp = (Nmue_v_Te[:,1]/nAr)*V0*tau/(L*L)
+    mue_spline = CubicSpline(Te_trans, mue_interp)
+    mue_Te_spline = CubicSpline.derivative(mue_spline)
+    mobility = Mobility(interpolate = True, mu_expression = mue_spline, mu_T_expression = mue_Te_spline)
+    muList.append(mobility)
+
+    Ns = 6
+    for i in range(1, Ns):
+        muList.append(Mobility(interpolate = False))
+
+    params.mobilityList = muList
+
+    # 5) Dump to screen
+    params.print()
diff --git a/psaapProperties_6Species_Sampling_2Torr_Expanded.py b/psaapProperties_6Species_Sampling_2Torr_Expanded.py
new file mode 100755
index 000000000..67b190b8d
--- /dev/null
+++ b/psaapProperties_6Species_Sampling_2Torr_Expanded.py
@@ -0,0 +1,494 @@
+import numpy as np
+from scipy.interpolate import CubicSpline
+import csv
+import matplotlib.pyplot as plt
+import matplotlib.colors as mcolors
+import h5py as h5
+import logging
+
+class Reaction(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Diffusivity(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Mobility(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+
+def setPsaapProperties_6Species_Sampling_2Torr_Expanded(gam, inputV0, inputVDC, params, Nr, iSample):
+    """Sets non-dimensional properties corresponding to Liu 2014 paper.
+
+    Inputs:
+      gam : Secondary electron emission coefficient
+      params : chebSolver.modelParams class
+
+    Outputs: None
+      params data is overwritten using values from Liu 2014.
+    """
+    ###################################################################
+    # User specified parameters (you may change these if you wish to
+    # run a different scenario from Liu 2014)
+    ###################################################################
+
+    # densities
+    nAr = 8.05e22     # background number density of Ar [1/m^3] (corresponds to p = 2.5 Torr)
+    np0 = 8e16        # "nominal" electron density [1/m^3]
+
+    # masses
+    # me = 9.10938356e-31        # mass of an electron [kg]
+    # me = 5.489e-4              # mass of an electron [u]
+    me = 0.511e6                 # mass of an electron [eV/c2]
+    # mAr = 39.948               # mass of an argon atom [u]
+    # mAr = 39.948 * 1.66054e-27 # mass of an argon atom [kg]
+    mAr = 37.2158e9              # mass of an electron [eV/c2]
+    # u = 931.4941e6             # eV/c2
+    c = 299792458                # speed of light [m/s]
+    se = 40                      # momentum cross section [A^2]
+
+    # nominal electron energy
+    e0 = 1.0  # [eV]
+
+    # pressure
+    p  = 333.3*1.5      # [J/m^3] *1.5 to convert it to energy (2.5 Torr)
+
+    # gas energy at the wall
+    Tg0 = 0.038778    # 3/2*300K*kB ~ (p0 - nT[:,0])/ntot
+
+    # characteristics of driving voltage
+    V0  = inputV0                 # amplitude of driving voltage [V]
+    verticalShift = inputVDC      # DC voltage (vertical shift in driving voltage)
+    tau = (1./13.56e6)             # period of driving voltage [s]
+    L   = 2.00*0.005              # half-gap-width [m] (gap width is 2 cm)
+    electrodeArea = np.pi*0.05**2 # electrode area [m^2] (electrode diameter = 0.1 m)
+
+    # Add voltage uncertainty
+    V0 += h5.File('../../../BOLSIGChemistry_Voltage/Voltage.%08d.h5' % (iSample), 'r')["V_Err"][0]
+
+    # transport parameters
+    nmue = 9.66e21   # argon number density times electron mobility [1/(V*cm*s)]
+    nmum = 0.0
+    nmui = 8.0e19
+    nDe  = 3.86e22   # argon number density times electron diffusivity [1/(cm*s)]
+    nDi  = 2.07e18   # argon number density times ion diffusivity [1/(cm*s)]
+    nDm  = 2.42e18   # argon number density times metastable diffusivity [1/(cm*s)]
+
+    # reaction parameters (NB: k_i = Ck*Ee^B*exp(-A/Ee))
+    #                          Ee = 3/2*Te (Te in eV)
+    #                          -> k_i = [Ck*(2/3)^B] * Ee^B * exp[-(3/2)*A/Ee]
+    # nominal
+    Ck = np.array([2.0e-13,2.1e-15,5.0e-16,6.4e-16,2.1e-21,1.32e8,1.72e7,1.50e7,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0e-18,4.0e-19,0.0,0.0,0.0,0.0,2.5e-17,2.5e-17,1.0e-15,1.0e-15,0.0,0.0]) # pre-exponential factors [m^3/s]
+    B  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-0.5,0,0,0,0,0,0,0,0,0,0])
+    A  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]) # activation temperature [eV]
+    dH = np.array([0.0,-7.541,-10.577,-7.393,0.0,0.0,0.0,0.0,11.577,11.725,13.168,15.76,-11.577,4.183,0.148,1.592,2.592,-1.444,-1.592,-11.725,-0.148,1.444,0.0,0.0,-4.183,-4.035,-2.592,-15.76,0.0,0.0,-8.985,-9.133,4.035,-13.168]) # energy lost per electron due to ionization rxn [eV]
+    dEps = np.array([0.0,15.76,11.577,11.725,13.168,0.0])
+
+    # BC parameters
+    # ks = 1.19e7  # electron recombination rate [cm/s]
+    ks = 1.366109824889323e7 # electron recombination rate [cm/s/eV]
+
+    ###################################################################
+    # Constants of nature (probably shouldn't change unless you have
+    # root privileges on universe)
+    ###################################################################
+    qe   = 1.6e-19   # unit charge [C]
+    eps0 = 8.86e-12  # permittivity of free space [F/m]
+    kB   = 1.38e-23  # Boltzmann constant [J/K]
+    # kB   = 8.62e−5 # Boltzmann constant [eV/K]
+
+
+    ###################################################################
+    # Calculate non-dimensional parameters
+    ###################################################################
+
+    # 1) Convert input units to base SI (except eV)
+    nDe  *= 100. # 1/(m*s)
+    nDi  *= 100. # 1/(m*s)
+    nDm  *= 100.
+    nmue *= 100. # 1/(V*m*s)
+    nmui *= 100. # 1/(V*m*s)
+    ks   *= 0.01 # m/s
+    se   *= 1.0e-20  # m^2
+
+    # 2) Compute "raw" transport parameters
+    De  = nDe/nAr
+    Di  = nDi/nAr
+    Dm  = nDm/nAr
+
+    mue = nmue/nAr
+    mui = nmui/nAr
+    mum = nmum/nAr
+
+    # 3) Compute non-dimensional properties required by solver
+    De    = De*tau/(L*L)
+    Di    = Di*tau/(L*L)
+    Dm    = Dm*tau/(L*L)
+
+    mue   = mue*V0*tau/(L*L)
+    mui   = mui*V0*tau/(L*L)
+    mum   = mum*V0*tau/(L*L)
+
+    Ck[0:4]  *= tau*np0
+    Ck[4]    *= tau*nAr
+    Ck[5:8]  *= tau
+    Ck[8:12] *= tau*nAr
+    Ck[12:24]  *= tau*np0
+    Ck[24:28] *= tau*np0*np0
+    Ck[28:30] *= tau*nAr
+    Ck[30:] *= tau*np0
+    A       = A*1.5/e0  # 1.5 to convert from temperature to energy
+    dH      = dH/e0
+    qStar   = V0/e0 # qe*V0/e0, since e0 in eV, need qe*V0 in eV, which is just V0 in V
+    alpha   = qe*np0*L*L/(V0*eps0)
+    ks      = ks*tau/L
+    p0      = p/qe/np0
+    kappaB  = 4.878171165833662*1.6129 # non-dimensional thermal conductivity of background specie
+                                # (2/3)*tau/L**2*Kb/np0/kB,
+                                # where Kb is the thermal conductivity of background specie
+
+    params.beta = np.array([[0,1,1,1,0,0,0,0,1,1,1,2,1,2,1,1,2,1,1,1,1,1,0,0,1,1,1,1,0,0,1,1,2,1],                     # E
+                            [0,1,1,1,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0],                     # AR+
+                            [0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,1,0,0,0,0,0],                     # AR(m)
+                            [0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0],                     # AR(r)
+                            [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0],                     # AR(4p)
+                            [2,1,1,1,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1]], dtype=np.int64)    # AR
+
+    params.alfa = np.array([[0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,0,0,0,0,1,1],                     # E
+                            [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0],                     # AR+
+                            [2,1,0,2,1,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],                     # AR(m)
+                            [0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,1,0],                     # AR(r)
+                            [0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,0,1],                     # AR(4p)
+                            [0,0,0,0,1,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0]], dtype=np.int64)    # AR
+    
+    # Rxn1:  2AR(m)         ->   2AR
+    # Rxn2:  AR(m) + AR(r)  ->   E + AR+ + AR
+    # Rxn3:  2AR(4p)        ->   E + AR+ + AR
+    # Rxn4:  2AR(m)         ->   E + AR+ + AR
+    # Rxn5:  AR(m) + AR     ->   2AR
+    # Rxn6:  AR(r)          ->   AR + hv
+    # Rxn7:  AR(4p)         ->   AR(m) + hv
+    # Rxn8:  AR(4p)         ->   AR(r) + hv
+    # Rxn9:  E + AR         ->   E + AR(m)
+    # Rxn10: E + AR         ->   E + AR(r)
+    # Rxn11: E + AR         ->   E + AR(4p)
+    # Rxn12: E + AR         ->   2E + AR+
+    # Rxn13: E + AR(m)      ->   E + AR
+    # Rxn14: E + AR(m)      ->   2E + AR+
+    # Rxn15: E + AR(m)      ->   E + AR(r)
+    # Rxn16: E + AR(m)      ->   E + AR(4p)
+    # Rxn17: E + AR(4p)     ->   2E + AR+
+    # Rxn18: E + AR(4p)     ->   E + AR(r)
+    # Rxn19: E + AR(4p)     ->   E + AR(m)
+    # Rxn20: E + AR(r)      ->   E + AR
+    # Rxn21: E + AR(r)      ->   E + AR(m)
+    # Rxn22: E + AR(r)      ->   E + AR(4p)
+    # Rxn23: E + AR+        ->   AR(m) + hv
+    # Rxn24: E + AR+        ->   AR(4p) + hv
+    # Rxn25: 2E + AR+       ->   E + AR(m)
+    # Rxn26: 2E + AR+       ->   E + AR(r)
+    # Rxn27: 2E + AR+       ->   E + AR(4p)
+    # Rxn28: 2E + AR+       ->   E + AR
+    # Rxn29: AR + AR(4p)    ->   AR + AR(m)
+    # Rxn30: AR + AR(4p)    ->   AR + AR(r)
+    # Rxn31: AR(m) + AR(4p) ->   E + AR+ + AR
+    # Rxn32: AR(r) + AR(4p) ->   E + AR+ + AR
+    # Rxn33: E + AR(r)      ->   2E + AR+
+    # Rxn34: E + AR(4p)     ->   E + AR 
+
+    rxnNameDict = {0: "Ar(m) + Ar(m) => Ar + Ar",
+                   1: "Ar(m) + Ar(r) => E + Ar + Ar+",
+                   2: "Ar(4p) + Ar(4p) => E + Ar + Ar+",
+                   3: "Ar(m) + Ar(m) => E + Ar + Ar+",
+                   4: "Ar + Ar(m) => Ar + Ar",
+                   5: "Ar(r) => Ar",
+                   6: "Ar(4p) => Ar(m)",
+                   7: "Ar(4p) => Ar(r)",
+                   8: "Excitation_Metastable",
+                   9: "Excitation_Resonant",
+                   10: "Excitation_4p",
+                   11: "Ionization",
+                   12: "DeExcitation_Metastable",
+                   13: "StepIonization_Metastable",
+                   14: "E + Ar(m) => E + Ar(r)",
+                   15: "E + Ar(m) => E + Ar(4p)",
+                   16: "StepIonization_4p",
+                   17: "E + Ar(4p) => E + Ar(r)",
+                   18: "E + Ar(4p) => E + Ar(m)",
+                   19: "DeExcitation_Resonant",
+                   20: "E + Ar(r) => E + Ar(m)",
+                   21: "E + Ar(r) => E + Ar(4p)",
+                   22: "E + Ar+ => Ar(m)",
+                   23: "E + Ar+ => Ar(4p)",
+                   24: "3BdyRecomb_Metastable",
+                   25: "3BdyRecomb_Resonant",
+                   26: "3BdyRecomb_4p",
+                   27: "3BdyRecomb_Ground",
+                   28: "Ar + Ar(4p) => Ar + Ar(m)",
+                   29: "Ar + Ar(4p) => Ar + Ar(r)",
+                   30: "Ar(m) + Ar(4p) => E + Ar+ + Ar",
+                   31: "Ar(r) + Ar(4p) => E + Ar+ + Ar",
+                   32: "StepIonization_Resonant",
+                   33: "DeExcitation_4p"}
+
+    # 4) Set values in params class
+    params.D[0]    = De
+    params.D[1]    = Di
+    params.D[2]    = Dm
+
+    params.mu[0]   = mue
+    params.mu[1]   = mui
+    params.mu[2]   = mum
+
+    params.A[:]    = Ck[:]
+    params.B[:]    = B[:]
+    params.C[:]    = A[:]
+
+    # Account for the 2/3 term to convert from electron temperature to electron energy
+    for i in range(len(params.A)):
+        params.A[i] *= (2/3)**(params.B[i])
+
+    params.dH[:]   = dH[:]
+    params.dEps[:] = dEps[:]
+    params.qStar   = qStar
+    params.alpha   = alpha
+    params.ks      = ks
+    params.gam     = gam
+    params.kappaB  = kappaB
+    params.nAronp0 = nAr / np0
+    params.p0      = p0
+    params.Tg0     = Tg0
+    params.EC      = 2.0 * me / mAr \
+        * np.sqrt(16.0 * (me + mAr) * e0 * c**2
+                  / (3.0 * np.pi * me * mAr)) * se * nAr * tau
+    # params.EC = 2.0 * me / mAr * 3.8e9 * tau
+
+    params.verticalShift = verticalShift / V0
+
+    # Parameters needed to compute the current with dimensions
+    params.V0Ltau  = V0 / (L * tau)
+    params.V0L     = V0 / L
+    params.LLV0tau = (L*L) / (V0*tau)
+    params.tauL    = L / tau
+    params.np0     = np0           # "nominal" electron density [1/m^3]
+    params.qe      = qe            # unit charge [C]
+    params.eps0    = eps0          # unit charge [C]
+    params.eArea   = electrodeArea # electrode area [m^2]
+
+
+    reactionExpressionTypelist =  np.array([False,False,False,False,False,False,False,False,
+                                           True,True,True,True,True,True,True,True,True,True,True,True,True,True,
+                                           False,False,True,True,True,True,False,False,False,False,True,True])
+
+    thresholded_rxn = np.array([False, False, False, False, False, False, False, False,
+                                True, True, True, True, False, True, True, True, True, False, False, False, False,
+                                True, False, False, False, False, False, False, False, False, False, False, True,
+                                False])
+    reactionsList = []
+    LOGFilename = 'interpolationSample%s.log'%str(iSample)
+    f = open(LOGFilename, 'w')
+
+    for i in range(Nr):
+        sample_root_dir = "../../../BOLSIGChemistry_6SpeciesRates"
+        if reactionExpressionTypelist[i]:
+            if i < 14 or i == 16 or i == 19 or i > 23:
+                fileString = sample_root_dir + "/" + rxnNameDict[i]
+                fileName = "%s.%08d.h5" % (fileString, iSample)
+                f = h5.File(fileName, 'r')
+                dataset = f["table"]
+            else:
+                fileString = sample_root_dir + "/" + "StepwiseExcitations"
+                fileName = "%s.%08d.h5" % (fileString, iSample)
+                f = h5.File(fileName, 'r')
+                dataset = f[rxnNameDict[i]]
+            
+            rateCoeff = dataset[:,1]
+            if i > 23 and i < 28:
+                rateCoeff /= 6.022e23**2
+            else:
+                rateCoeff /= 6.022e23
+            Te = dataset[:,0]
+            Te /= 11604.
+
+            ## Removing BOLSIG failures
+            fail_inds = []
+            for j in range(len(rateCoeff)):
+                if rateCoeff[j] == 0.0 and j > np.nonzero(rateCoeff)[0][0]:
+                    fail_inds.append(j)
+
+            Te = np.delete(Te, fail_inds)
+            rateCoeff = np.delete(rateCoeff, fail_inds)
+
+            # Sorting mean energy array and rate coefficient array based on
+            # the mean energy array.
+            Teinds = Te.argsort()
+            rateCoeff = rateCoeff[Teinds]
+            Te = Te[Teinds]
+
+            # Find duplicates
+            TeDuplicateinds = np.where(np.abs(np.diff(Te, axis=0)) > 0.0)
+            TeDuplicateindsForLog = np.where(np.abs(np.diff(Te, axis=0)) == 0.0)
+            rateCoeff = rateCoeff[TeDuplicateinds]
+            Te = Te[TeDuplicateinds]
+
+            # Nondimensionalization of mean energy.
+            Te *= 1.5
+
+            # Find first non-zero value of the coefficient rate.
+            I = np.nonzero(rateCoeff)
+
+            diffRateCoeff = [j-i for i, j in zip(rateCoeff[:-1], rateCoeff[1:])]
+            diffTe = [j-i for i, j in zip(Te[:-1], Te[1:])]
+
+            Monotonicity = np.asarray([j/i for i, j in zip(diffTe, diffRateCoeff)])
+            Monotonicity = np.insert(Monotonicity, 0, 0.0, axis=0)
+
+            Nan = np.isnan(Monotonicity)
+            Inf = np.isinf(Monotonicity)
+            if thresholded_rxn[i] == True:
+                indexPositive = np.where(Monotonicity>0.0)
+            else:
+                indexPositive = np.where(Monotonicity<0.0)
+            Positive = np.full(Monotonicity.shape, False, dtype=bool)
+            Positive[indexPositive] = True
+
+            indices = Nan + Inf + Positive
+
+            #lastFalse = np.where(indices==False)[-1][-1] + 2
+            for k in range(len(Te)):
+                if (Te[k] < 4.5 and indices[k] == False):
+                    lastFalse = k + 2
+
+            # Transformation to log scale.
+            TeLog = np.log(Te)
+
+            # Compute the slope of the rate coefficient between its first two non-zero values.
+            # Finite differences are used.
+            dydx = (rateCoeff[lastFalse + 1] - rateCoeff[lastFalse]) \
+                 / (Te[lastFalse + 1] - Te[lastFalse])
+
+            # Arrhenius form: kf = A * exp(-C / Te)
+            C = Te[lastFalse]**2.0*dydx / rateCoeff[lastFalse]
+
+            # Compute pre-exponential coefficient, A, in log scale.
+            ALog = np.log(rateCoeff[lastFalse]) + C / Te[lastFalse]
+
+            # Transform rate coefficient in log scale.
+            rateCoeffLog = np.zeros(rateCoeff.shape)
+            rateCoeffLog[lastFalse:] = np.log(rateCoeff[lastFalse:])
+            # For the troublesome values, we use the Arrhenius form.
+            rateCoeffLog[0:lastFalse] = ALog - C / Te[0:lastFalse]
+            # Nondimensionalization in log scale.
+            if i < 12:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
+            elif i > 23 and i < 28:
+                rateCoeffLog += - np.log(1.0/tau) + 2*np.log(np0)
+            else:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(np0)
+
+            # Interpolation in log scale.
+            reactionExpressionsLog = CubicSpline(TeLog, rateCoeffLog)
+            # Gradient in log scale
+            reactionTExpressionsLog = CubicSpline.derivative(reactionExpressionsLog)
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf_log = reactionExpressionsLog,
+                                kf_T_log = reactionTExpressionsLog)
+            reactionsList.append(reaction)
+
+            logging.basicConfig(filename=LOGFilename)
+            logging.warning('Interpolation info (Reaction %s):', i)
+            logging.warning('First non-zero entry in the rate coefficient: %s', I[0][0])
+            logging.warning('Monotonicity of the rate coefficient start from entry: %s', lastFalse)
+            logging.warning('Position of possible duplicates in mean energy array: %s',
+                          TeDuplicateindsForLog[0])
+
+        else:
+            fileName = "%s/Arrhenius.%08d.h5" % (sample_root_dir, iSample)
+            f = h5.File(fileName, 'r')
+            arrh_Coeffs = f[rxnNameDict[i]][...]
+            A, B, C = arrh_Coeffs
+
+            # Non-dimensionalize the coefficients
+            if i < 4:
+                A /= 6.022e23
+                A *= tau*np0
+            elif i == 4:
+                A /= 6.022e23
+                A *= tau*nAr
+            elif i > 4 and i < 8:
+                A *= tau
+            elif i > 27 and i < 30:
+                A /= 6.022e23
+                A *= tau*nAr
+            else:
+                A /= 6.022e23
+                A *= tau*np0
+
+            A *= ((2./3.)*11604)**B 
+            C *= 1.5/(e0*11604)
+
+            rxn = eval("lambda energy :" + f"{A} * energy**{B} * np.exp(-{C} / energy)")
+            rxn_T = eval("lambda energy :" + f"{A} * energy**({B}-1) * np.exp(-{C} / energy) * ({B} + {C} / energy)")
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf = rxn, kf_T = rxn_T)
+            reactionsList.append(reaction)
+
+    params.reactionsList = reactionsList
+
+    diffList = []
+    # Data from BOLSIG
+    # Te in [eV]
+    # De * N in [1/(m*s)]
+    transport = h5.File("../../../BOLSIGChemistry_6SpeciesRates/Transport.%08d.h5" % (iSample), 'r')
+    NDe_v_Te = transport["diffusivity"]
+    Te_trans = NDe_v_Te[:,0]
+    Te_trans /= 11604
+    De_interp = (NDe_v_Te[:,1]/nAr)*tau/(L*L)
+    De_spline = CubicSpline(Te_trans, De_interp)
+    De_Te_spline = CubicSpline.derivative(De_spline)
+    diffusivity = Diffusivity(interpolate = True, D_expression = De_spline, D_T_expression = De_Te_spline)
+    diffList.append(diffusivity)
+
+    Ns = 6
+    for i in range(1, Ns):
+        diffList.append(Diffusivity(interpolate = False))
+
+    params.diffusivityList = diffList
+
+    muList = []
+    # Data from BOLSIG
+    # Te in [eV]
+    # Mue * N in [1/(V*m*s)]
+    Nmue_v_Te = transport["mobility"]
+    mue_interp = (Nmue_v_Te[:,1]/nAr)*V0*tau/(L*L)
+    mue_spline = CubicSpline(Te_trans, mue_interp)
+    mue_Te_spline = CubicSpline.derivative(mue_spline)
+    mobility = Mobility(interpolate = True, mu_expression = mue_spline, mu_T_expression = mue_Te_spline)
+    muList.append(mobility)
+
+    Ns = 6
+    for i in range(1, Ns):
+        muList.append(Mobility(interpolate = False))
+
+    params.mobilityList = muList
+
+    # 5) Dump to screen
+    params.print()
diff --git a/psaapProperties_6Species_Sampling_500mTorr.py b/psaapProperties_6Species_Sampling_500mTorr.py
new file mode 100755
index 000000000..71f69200d
--- /dev/null
+++ b/psaapProperties_6Species_Sampling_500mTorr.py
@@ -0,0 +1,499 @@
+import numpy as np
+from scipy.interpolate import CubicSpline
+import csv
+import matplotlib.pyplot as plt
+import matplotlib.colors as mcolors
+import h5py as h5
+import logging
+
+class Reaction(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Diffusivity(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Mobility(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+
+def setPsaapProperties_6Species_Sampling_500mTorr(gam, inputV0, inputVDC, params, Nr, iSample):
+    """Sets non-dimensional properties corresponding to Liu 2014 paper.
+
+    Inputs:
+      gam : Secondary electron emission coefficient
+      params : chebSolver.modelParams class
+
+    Outputs: None
+      params data is overwritten using values from Liu 2014.
+    """
+    ###################################################################
+    # User specified parameters (you may change these if you wish to
+    # run a different scenario from Liu 2014)
+    ###################################################################
+
+    # densities
+    nAr = 1.61e22     # background number density of Ar [1/m^3] (corresponds to p=100 mTorr)
+    np0 = 8e16        # "nominal" electron density [1/m^3]
+
+    # masses
+    # me = 9.10938356e-31        # mass of an electron [kg]
+    # me = 5.489e-4              # mass of an electron [u]
+    me = 0.511e6                 # mass of an electron [eV/c2]
+    # mAr = 39.948               # mass of an argon atom [u]
+    # mAr = 39.948 * 1.66054e-27 # mass of an argon atom [kg]
+    mAr = 37.2158e9              # mass of an electron [eV/c2]
+    # u = 931.4941e6             # eV/c2
+    c = 299792458                # speed of light [m/s]
+    se = 40                      # momentum cross section [A^2]
+
+    # nominal electron energy
+    e0 = 1.0  # [eV]
+
+    # pressure
+    p  = 66.67*1.5      # [J/m^3] *1.5 to convert it to energy (1 Torr)
+
+    # gas energy at the wall
+    Tg0 = 0.038778    # 3/2*300K*kB ~ (p0 - nT[:,0])/ntot
+
+    # characteristics of driving voltage
+    V0  = inputV0                 # amplitude of driving voltage [V]
+    verticalShift = inputVDC      # DC voltage (vertical shift in driving voltage)
+    tau = (1./13.56e6)             # period of driving voltage [s]
+    L   = 2.00*0.005              # half-gap-width [m] (gap width is 2 cm)
+    electrodeArea = np.pi*0.05**2 # electrode area [m^2] (electrode diameter = 0.1 m)
+
+    # transport parameters
+    nmue = 9.66e21   # argon number density times electron mobility [1/(V*cm*s)]
+    nmum = 0.0
+    nmui = 8.0e19
+    nDe  = 3.86e22   # argon number density times electron diffusivity [1/(cm*s)]
+    nDi  = 2.07e18   # argon number density times ion diffusivity [1/(cm*s)]
+    nDm  = 2.42e18   # argon number density times metastable diffusivity [1/(cm*s)]
+
+    # reaction parameters (NB: k_i = Ck*Ee^B*exp(-A/Ee))
+    #                          Ee = 3/2*Te (Te in eV)
+    #                          -> k_i = [Ck*(2/3)^B] * Ee^B * exp[-(3/2)*A/Ee]
+    # nominal
+    Ck = np.array([2.0e-7,2.1e-9,5.0e-10,6.4e-10,2.1e-15,1.32e8,0.0,3.0e7,3.0e7,0.0,0.0,0.0,0.0,4.3e-10,0.0,3.7e-8,8.9e-7,1.8e-7,3.0e-7,3.0e-7,4.3e-10,9.1e-7,8.9e-7]) # pre-exponential factors [cm^3/s]
+    B  = np.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.74,0.0,0.0,0.51,0.61,0.51,0.51,0.74,0.0,0.51])
+    A  = np.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.59,2.61,0.0,0.0,0.0,0.0,1.59]) # activation temperature [eV]
+    dH = np.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.548,11.624,12.907,15.76,-11.548,4.212,0.076,1.359,2.853,-1.283,-1.359,-11.624,-0.076,0.983]) # energy lost per electron due to ionization rxn [eV]
+    dEps = np.array([0.0,15.76,11.548,11.624,12.907,0.0])
+
+    # BC parameters
+    # ks = 1.19e7  # electron recombination rate [cm/s]
+    ks = 1.366109824889323e7 # electron recombination rate [cm/s/eV]
+
+    ###################################################################
+    # Constants of nature (probably shouldn't change unless you have
+    # root privileges on universe)
+    ###################################################################
+    qe   = 1.6e-19   # unit charge [C]
+    eps0 = 8.86e-12  # permittivity of free space [F/m]
+    kB   = 1.38e-23  # Boltzmann constant [J/K]
+    # kB   = 8.62e−5 # Boltzmann constant [eV/K]
+
+
+    ###################################################################
+    # Calculate non-dimensional parameters
+    ###################################################################
+
+    # 1) Convert input units to base SI (except eV)
+    nDe  *= 100. # 1/(m*s)
+    nDi  *= 100. # 1/(m*s)
+    nDm  *= 100.
+    nmue *= 100. # 1/(V*m*s)
+    nmui *= 100. # 1/(V*m*s)
+    Ck[0:5]  *= 1e-6 # m^3/s
+    Ck[5:9]  *= 1 # 1/s
+    Ck[9:]   *= 1e-6 # m^6/s
+    ks   *= 0.01 # m/s
+    se   *= 1.0e-20  # m^2
+
+    # 2) Compute "raw" transport parameters
+    De  = nDe/nAr
+    Di  = nDi/nAr
+    Dm  = nDm/nAr
+
+    mue = nmue/nAr
+    mui = nmui/nAr
+    mum = nmum/nAr
+
+    # 3) Compute non-dimensional properties required by solver
+    De    = De*tau/(L*L)
+    Di    = Di*tau/(L*L)
+    Dm    = Dm*tau/(L*L)
+
+    mue   = mue*V0*tau/(L*L)
+    mui   = mui*V0*tau/(L*L)
+    mum   = mum*V0*tau/(L*L)
+
+    Ck[0:4]  *= tau*np0
+    Ck[4]    *= tau*nAr
+    Ck[5:9]  *= tau
+    Ck[9:13] *= tau*nAr
+    Ck[13:]  *= tau*np0
+    A       = A*1.5/e0  # 1.5 to convert from temperature to energy
+    dH      = dH/e0
+    qStar   = V0/e0 # qe*V0/e0, since e0 in eV, need qe*V0 in eV, which is just V0 in V
+    alpha   = qe*np0*L*L/(V0*eps0)
+    ks      = ks*tau/L
+    p0      = p/qe/np0
+    kappaB  = 4.878171165833662*1.6129 # non-dimensional thermal conductivity of background specie
+                                # (2/3)*tau/L**2*Kb/np0/kB,
+                                # where Kb is the thermal conductivity of background specie
+
+    params.beta = np.array([[0,1,1,1,0,0,0,0,0,1,1,1,2,1,2,1,1,2,1,1,1,1,1],                     # E
+                            [0,1,1,1,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0],                     # AR+
+                            [0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0],                     # AR(m)
+                            [0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0],                     # AR(r)
+                            [0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1],                     # AR(4p)
+                            [2,1,1,1,2,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0]], dtype=np.int64)    # AR
+
+    params.alfa = np.array([[0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1],                     # E
+                            [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],                     # AR+
+                            [2,1,0,2,1,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0],                     # AR(m)
+                            [0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1],                     # AR(r)
+                            [0,0,2,0,0,0,1,1,1,0,0,0,0,0,0,0,0,1,1,1,0,0,0],                     # AR(4p)
+                            [0,0,0,0,1,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0]], dtype=np.int64)    # AR
+            # Rxn1:  2AR(m)         ->   2AR
+            # Rxn2:  AR(m) + AR(r)  ->   E + AR+ + AR
+            # Rxn3:  2AR(4p)        ->   E + AR+ + AR
+            # Rxn4:  2AR(m)         ->   E + AR+ + AR
+            # Rxn5:  AR(m) + AR     ->   2AR
+            # Rxn6:  AR(r)          ->   AR
+            # Rxn7:  AR(4p)         ->   AR
+            # Rxn8:  AR(4p)         ->   AR(m)
+            # Rxn9:  AR(4p)         ->   AR(r)
+            # Rxn10: E + AR         ->   E + AR(m)
+            # Rxn11: E + AR         ->   E + AR(r)
+            # Rxn12: E + AR         ->   E + AR(4p)
+            # Rxn13: E + AR         ->   2E + AR+
+            # Rxn14: E + AR(m)      ->   E + AR
+            # Rxn15: E + AR(m)      ->   2E + AR+
+            # Rxn16: E + AR(m)      ->   E + AR(r)
+            # Rxn17: E + AR(m)      ->   E + AR(4p)
+            # Rxn18: E + AR(4p)     ->   2E + AR+
+            # Rxn19: E + AR(4p)     ->   E + AR(r)
+            # Rxn20: E + AR(4p)     ->   E + AR(m)
+            # Rxn21: E + AR(r)      ->   E + AR
+            # Rxn22: E + AR(r)      ->   E + AR(m)
+            # Rxn23: E + AR(r)      ->   E + AR(4p)
+
+    rxnNameDict = {0: "Ar(m) + Ar(m) => Ar + Ar",
+                   1: "Ar(m) + Ar(r) => E + Ar + Ar+",
+                   2: "Ar(4p) + Ar(4p) => E + Ar + Ar+",
+                   3: "Ar(m) + Ar(m) => E + Ar + Ar+",
+                   4: "Ar + Ar(m) => Ar + Ar",
+                   5: "Ar(r) => Ar",
+                   6: "Ar(4p) => Ar",
+                   7: "Ar(4p) => Ar(m)",
+                   8: "Ar(4p) => Ar(r)",
+                   9: "1s-metastable",
+                   10: "1s-resonance",
+                   11: "2p-lumped",
+                   12: "Ionization",
+                   13: "E + Ar(m) => E + Ar",
+                   14: "StepIonization",
+                   15: "E + Ar(m) => E + Ar(r)",
+                   16: "E + Ar(m) => E + Ar(4p)",
+                   17: "E + Ar(4p) => E + E + Ar+",
+                   18: "E + Ar(4p) => E + Ar(r)",
+                   19: "E + Ar(4p) => E + Ar(m)",
+                   20: "E + Ar(r) => E + Ar",
+                   21: "E + Ar(r) => E + Ar(m)",
+                   22: "E + Ar(r) => E + Ar(4p)"}
+
+    # 4) Set values in params class
+    params.D[0]    = De
+    params.D[1]    = Di
+    params.D[2]    = Dm
+
+    params.mu[0]   = mue
+    params.mu[1]   = mui
+    params.mu[2]   = mum
+
+    params.A[:]    = Ck[:]
+    params.B[:]    = B[:]
+    params.C[:]    = A[:]
+
+    # Account for the 2/3 term to convert from electron temperature to electron energy
+    for i in range(len(params.A)):
+        params.A[i] *= (2/3)**(params.B[i])
+
+    params.dH[:]   = dH[:]
+    params.dEps[:] = dEps[:]
+    params.qStar   = qStar
+    params.alpha   = alpha
+    params.ks      = ks
+    params.gam     = gam
+    params.kappaB  = kappaB
+    params.nAronp0 = nAr / np0
+    params.p0      = p0
+    params.Tg0     = Tg0
+    params.EC      = 2.0 * me / mAr \
+        * np.sqrt(16.0 * (me + mAr) * e0 * c**2
+                  / (3.0 * np.pi * me * mAr)) * se * nAr * tau
+    # params.EC = 2.0 * me / mAr * 3.8e9 * tau
+
+    params.verticalShift = verticalShift / V0
+
+    # Parameters needed to compute the current with dimensions
+    params.V0Ltau  = V0 / (L * tau)
+    params.V0L     = V0 / L
+    params.LLV0tau = (L*L) / (V0*tau)
+    params.tauL    = L / tau
+    params.np0     = np0           # "nominal" electron density [1/m^3]
+    params.qe      = qe            # unit charge [C]
+    params.eps0    = eps0          # unit charge [C]
+    params.eArea   = electrodeArea # electrode area [m^2]
+
+
+    reactionExpressionTypelist =  np.array([False,False,False,False,False,False,False,False,False,
+                                           True,True,True,True,False,True,True,True,False,True,True,False,True,True])
+
+    reactionsList = []
+    LOGFilename = 'interpolationSample%s.log'%str(iSample)
+    f = open(LOGFilename, 'w')
+
+    for i in range(Nr):
+        sample_root_dir = "../../BOLSIGChemistry_6SpeciesRates"
+        if reactionExpressionTypelist[i]:
+            if (i < 15):
+                fileString = sample_root_dir + "/" + rxnNameDict[i]
+                fileName = "%s.%08d.h5" % (fileString, iSample)
+                f = h5.File(fileName, 'r')
+                dataset = f["table"]
+            else:
+                fileString = sample_root_dir + "/" + "StepwiseExcitations"
+                fileName = "%s.%08d.h5" % (fileString, iSample)
+                f = h5.File(fileName, 'r')
+                dataset = f[rxnNameDict[i]]
+            
+            rateCoeff = dataset[:,1]
+            rateCoeff /= 6.022e23
+            Te = dataset[:,0]
+            Te /= 11604.
+
+            # Sorting mean energy array and rate coefficient array based on
+            # the mean energy array.
+            Teinds = Te.argsort()
+            rateCoeff = rateCoeff[Teinds]
+            Te = Te[Teinds]
+
+            # Find duplicates
+            TeDuplicateinds = np.where(np.abs(np.diff(Te, axis=0)) > 0.0)
+            TeDuplicateindsForLog = np.where(np.abs(np.diff(Te, axis=0)) == 0.0)
+            rateCoeff = rateCoeff[TeDuplicateinds]
+            Te = Te[TeDuplicateinds]
+
+            # Nondimensionalization of mean energy.
+            Te *= 1.5
+
+            # Find first non-zero value of the coefficient rate.
+            I = np.nonzero(rateCoeff)
+
+            diffRateCoeff = [j-i for i, j in zip(rateCoeff[:-1], rateCoeff[1:])]
+            diffTe = [j-i for i, j in zip(Te[:-1], Te[1:])]
+
+            Monotonicity = np.asarray([j/i for i, j in zip(diffTe, diffRateCoeff)])
+            Monotonicity = np.insert(Monotonicity, 0, 0.0, axis=0)
+
+            Nan = np.isnan(Monotonicity)
+            Inf = np.isinf(Monotonicity)
+            indexPositive = np.where(Monotonicity>0.0)
+            Positive = np.full(Monotonicity.shape, False, dtype=bool)
+            Positive[indexPositive] = True
+
+            indices = Nan + Inf + Positive
+
+            #lastFalse = np.where(indices==False)[-1][-1] + 2
+            for k in range(len(Te)):
+                if (Te[k] < 4.5 and indices[k] == False):
+                    lastFalse = k + 2
+
+            # Transformation to log scale.
+            TeLog = np.log(Te)
+
+            # Compute the slope of the rate coefficient between its first two non-zero values.
+            # Finite differences are used.
+            dydx = (rateCoeff[lastFalse + 1] - rateCoeff[lastFalse]) \
+                 / (Te[lastFalse + 1] - Te[lastFalse])
+
+            # Arrhenius form: kf = A * exp(-C / Te)
+            C = Te[lastFalse]**2.0*dydx / rateCoeff[lastFalse]
+
+            # Compute pre-exponential coefficient, A, in log scale.
+            ALog = np.log(rateCoeff[lastFalse]) + C / Te[lastFalse]
+
+            # Transform rate coefficient in log scale.
+            rateCoeffLog = np.zeros(rateCoeff.shape)
+            rateCoeffLog[lastFalse:] = np.log(rateCoeff[lastFalse:])
+            # For the troublesome values, we use the Arrhenius form.
+            rateCoeffLog[0:lastFalse] = ALog - C / Te[0:lastFalse]
+            # Nondimensionalization in log scale.
+            if i == 9:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
+            elif i == 10:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
+            elif i == 11:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
+            elif i == 12:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
+            else:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(np0)
+
+            # Interpolation in log scale.
+            reactionExpressionsLog = CubicSpline(TeLog, rateCoeffLog)
+            # Gradient in log scale
+            reactionTExpressionsLog = CubicSpline.derivative(reactionExpressionsLog)
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf_log = reactionExpressionsLog,
+                                kf_T_log = reactionTExpressionsLog)
+            reactionsList.append(reaction)
+
+            logging.basicConfig(filename=LOGFilename)
+            logging.warning('Interpolation info (Reaction %s):', i)
+            logging.warning('First non-zero entry in the rate coefficient: %s', I[0][0])
+            logging.warning('Monotonicity of the rate coefficient start from entry: %s', lastFalse)
+            logging.warning('Position of possible duplicates in mean energy array: %s',
+                          TeDuplicateindsForLog[0])
+
+        else:
+            fileName = "%s/Arrhenius.%08d.h5" % (sample_root_dir, iSample)
+            f = h5.File(fileName, 'r')
+            arrh_Coeffs = f[rxnNameDict[i]][...]
+            A, B, C = arrh_Coeffs
+
+            # Non-dimensionalize the coefficients
+            if (i < 4):
+                A /= 6.022e23
+                A *= tau*np0
+            elif (i == 4):
+                A /= 6.022e23
+                A *= tau*nAr
+            elif (i > 4 and i < 9):
+                A *= tau
+            #elif (i > 8 and i < 13):
+            #    A /= 6.022e23
+            #    A *= tau*nAr
+            else:
+                A /= 6.022e23
+                A *= tau*np0
+
+            A *= ((2./3.)*11604)**B 
+            C *= 1.5/(e0*11604)
+
+            rxn = eval("lambda energy :" + f"{A} * energy**{B} * np.exp(-{C} / energy)")
+            rxn_T = eval("lambda energy :" + f"{A} * energy**({B}-1) * np.exp(-{C} / energy) * ({B} + {C} / energy)")
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf = rxn, kf_T = rxn_T)
+            reactionsList.append(reaction)
+
+    params.reactionsList = reactionsList
+
+    diffList = []
+    # Data from BOLSIG
+    # Te in [eV]
+    # De * N in [1/(m*s)]
+    #NDe_v_Te = np.array([ [-0.05, 1.8e25],         [0.0, 1.8e25],           [0.05, 1.8e25],          [0.1, 1.8e25],
+    #                      [0.2103718, 1.8e25],     [0.2244455, 1.8e25],     [0.2390528, 1.8e25],     [0.2544605, 1.8e25],
+    #                      [0.2710021, 1.8e25],     [0.2886776, 1.8e25],     [0.3078205, 1.8e25],     [0.3286309, 1.8e25],
+    #                      [0.3513089, 1.8e25],     [0.3760546, 1.8e25],     [0.4032682, 1.8e25],     [0.4331498, 1.8e25],
+    #                      [0.4659662, 1.8e25],     [0.5021843, 1.8e25],     [0.5424044, 1.8e25],     [0.5868266, 1.8e25],
+    #                      [0.6359845, 1.8e25],     [0.690345, 1.8e25],      [0.749041, 1.8e25],      [0.813073, 1.8e25],
+    #                      [1.217942, 1.46E+25],    [1.319326, 1.39E+25],    [1.430048, 1.32E+25],    [1.550108, 1.26E+25],
+    #                      [1.681507, 1.20E+25],    [1.823578, 1.15E+25],    [1.977655, 1.09E+25],    [2.143071, 1.05E+25],
+    #                      [2.319826, 9.98E+24],    [2.508587, 9.54E+24],    [2.709354, 9.13E+24],    [2.916124, 8.75E+24],
+    #                      [3.112889, 8.45E+24],    [3.272969, 8.24E+24],    [3.383691, 8.14E+24],    [3.456394, 8.09E+24],
+    #                      [3.505085, 8.07E+24],    [3.544438, 8.05E+24],    [3.579789, 8.03E+24],    [3.616474, 8.01E+24],
+    #                      [3.656494, 7.97E+24],    [3.701183, 7.93E+24],    [3.751875, 7.87E+24],    [3.807903, 7.81E+24],
+    #                      [3.871268, 7.75E+24],    [3.941303, 7.68E+24],    [4.018675, 7.61E+24],    [4.102717, 7.53E+24],
+    #                      [4.193429, 7.5E+24],     [4.290811, 7.5E+24],     [4.39553, 7.5E+24],      [4.507586, 7.5E+24],
+    #                      [5., 7.5E+24],           [6., 7.5E+24],           [7., 7.5E+24],           [8., 7.5E+24],
+    #                      [9., 7.5E+24],           [10., 7.5E+24],          [11., 7.5E+24],          [12., 7.5E+24]])
+
+    transport = h5.File("../../BOLSIGChemistry_6SpeciesRates/Transport.%08d.h5" % (iSample), 'r')
+    #Te = NDe_v_Te[:,0]
+    NDe_v_Te = transport["diffusivity"]
+    Te_trans = NDe_v_Te[:,0]
+    Te_trans /= 11604
+    print("Te_min = {0:.6e}".format(NDe_v_Te[0,0]))
+    print("Te_max = {0:.6e}".format(NDe_v_Te[-1,0]))
+    De_interp = (NDe_v_Te[:,1]/nAr)*tau/(L*L)
+    De_spline = CubicSpline(Te_trans, De_interp)
+    De_Te_spline = CubicSpline.derivative(De_spline)
+    diffusivity = Diffusivity(interpolate = True, D_expression = De_spline, D_T_expression = De_Te_spline)
+    diffList.append(diffusivity)
+
+    Ns = 6
+    for i in range(1, Ns):
+        diffList.append(Diffusivity(interpolate = False))
+
+    params.diffusivityList = diffList
+
+    muList = []
+    # Data from BOLSIG
+    # Te in [eV]
+    # Mue * N in [1/(V*m*s)]
+    #Nmue_v_Te = np.array([[0.02846756, 1.289e+26], [0.02975487, 1.33e+26],   [0.03173586, 1.383e+26],  [0.03477738,1.448e+26],
+    #                      [0.03937968, 1.523e+26], [0.04614973, 1.604e+26],  [0.05561446, 1.679e+26],  [0.0681674,1.735e+26],
+    #                      [0.0835084, 1.749e+26],  [0.1008504, 1.718e+26],   [0.1187927, 1.639e+26],   [0.1363348,1.522e+26],
+    #                      [0.1528764, 1.386e+26],  [0.1682841, 1.245e+26],   [0.1826913, 1.108e+26],   [0.1965649,9.809e+25],
+    #                      [0.2103718, 8.662e+25],  [0.2244455, 7.641e+25],   [0.2390528, 6.74e+25],    [0.2544605,5.947e+25],
+    #                      [0.2710021, 5.249e+25],  [0.2886776, 4.636e+25],   [0.3078205, 4.097e+25],   [0.3286309,3.623e+25],
+    #                      [0.3513089, 3.206e+25],  [0.3760546, 2.839e+25],   [0.4032682, 2.517e+25],   [0.4331498,2.232e+25],
+    #                      [0.4659662, 1.981e+25],  [0.5021843, 1.76e+25],    [0.5424044, 1.565e+25],   [0.5868266,1.392e+25],
+    #                      [0.6359845, 1.239e+25],  [0.690345, 1.103e+25],    [0.749041,  9.8e+24],     [0.813073,8.699e+24],
+    #                      [0.882441, 7.711e+24],   [0.957145, 6.828e+24],    [1.037185,  6.04e+24],    [1.123895,5.344e+24],
+    #                      [1.217942, 4.729e+24],   [1.319326, 4.186e+24],    [1.430048,  3.707e+24],   [1.550108,3.283e+24],
+    #                      [1.681507, 2.907e+24],   [1.823578, 2.574e+24],    [1.977655,  2.278e+24],   [2.143071,2.015e+24],
+    #                      [2.319826, 1.779e+24],   [2.508587, 1.569e+24],    [2.709354,  1.384e+24],   [2.916124,1.228e+24],
+    #                      [3.112889, 1.115e+24],   [3.272969, 1.055e+24],    [3.383691,  1.038e+24],   [3.456394,1.044e+24],
+    #                      [3.505085, 1.054e+24],   [3.544438, 1.062e+24],    [3.579789,  1.064e+24],   [3.616474, 1.059e+24],
+    #                      [3.656494, 1.048e+24],   [3.701183, 1.033e+24],    [3.751875,  1.014e+24],   [3.807903, 9.928e+23],
+    #                      [3.871268, 9.697e+23],   [3.941303,  9.459e+23],   [4.018675, 9.22e+23],     [4.102717, 8.991e+23],
+    #                      [4.193429, 8.773e+23],   [4.290811,  8.57e+23],    [4.39553, 8.383e+23],     [4.507586, 8.21e+23],
+    #                      [4.627646, 8.051e+23],   [4.757711,  7.901e+23],   [4.899115, 7.757e+23],    [5.054526, 7.617e+23],
+    #                      [5.227946, 7.479e+23],   [5.423377,  7.339e+23],   [5.646822, 7.198e+23],    [5.905618, 7.056e+23],
+    #                      [6.20977, 6.91e+23],     [6.571284,  6.757e+23],   [7.01017, 6.607e+23],     [7.54377, 6.458e+23],
+    #                      [8.19743, 6.303e+23],    [9.01117,   6.155e+23],   [10.02501, 6e+23],        [11.30565, 5.843e+23],
+    #                      [12.89978, 5.677e+23],   [14.91412,  5.51e+23],    [17.41537, 5.326e+23],    [20.57028, 5.149e+23],
+    #                      [24.51225, 4.968e+23],   [29.45472,  4.784e+23],   [35.6845, 4.602e+23],     [43.58845, 4.421e+23],
+    #                      [53.86692, 4.269e+23],   [67.367,    4.134e+23],   [85.4427, 4.026e+23],     [100.0, 4.026e+23]])
+
+    #Te = Nmue_v_Te[:,0]
+    Nmue_v_Te = transport["mobility"]
+    mue_interp = (Nmue_v_Te[:,1]/nAr)*V0*tau/(L*L)
+    mue_spline = CubicSpline(Te_trans, mue_interp)
+    mue_Te_spline = CubicSpline.derivative(mue_spline)
+    mobility = Mobility(interpolate = True, mu_expression = mue_spline, mu_T_expression = mue_Te_spline)
+    muList.append(mobility)
+
+    Ns = 6
+    for i in range(1, Ns):
+        muList.append(Mobility(interpolate = False))
+
+    params.mobilityList = muList
+
+    # 5) Dump to screen
+    params.print()
diff --git a/psaapProperties_6Species_Sampling_500mTorr_Expanded.py b/psaapProperties_6Species_Sampling_500mTorr_Expanded.py
new file mode 100755
index 000000000..23f0c1b0a
--- /dev/null
+++ b/psaapProperties_6Species_Sampling_500mTorr_Expanded.py
@@ -0,0 +1,494 @@
+import numpy as np
+from scipy.interpolate import CubicSpline
+import csv
+import matplotlib.pyplot as plt
+import matplotlib.colors as mcolors
+import h5py as h5
+import logging
+
+class Reaction(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Diffusivity(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Mobility(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+
+def setPsaapProperties_6Species_Sampling_500mTorr_Expanded(gam, inputV0, inputVDC, params, Nr, iSample):
+    """Sets non-dimensional properties corresponding to Liu 2014 paper.
+
+    Inputs:
+      gam : Secondary electron emission coefficient
+      params : chebSolver.modelParams class
+
+    Outputs: None
+      params data is overwritten using values from Liu 2014.
+    """
+    ###################################################################
+    # User specified parameters (you may change these if you wish to
+    # run a different scenario from Liu 2014)
+    ###################################################################
+
+    # densities
+    nAr = 1.61e22     # background number density of Ar [1/m^3] (corresponds to p = 500 mTorr)
+    np0 = 8e16        # "nominal" electron density [1/m^3]
+
+    # masses
+    # me = 9.10938356e-31        # mass of an electron [kg]
+    # me = 5.489e-4              # mass of an electron [u]
+    me = 0.511e6                 # mass of an electron [eV/c2]
+    # mAr = 39.948               # mass of an argon atom [u]
+    # mAr = 39.948 * 1.66054e-27 # mass of an argon atom [kg]
+    mAr = 37.2158e9              # mass of an electron [eV/c2]
+    # u = 931.4941e6             # eV/c2
+    c = 299792458                # speed of light [m/s]
+    se = 40                      # momentum cross section [A^2]
+
+    # nominal electron energy
+    e0 = 1.0  # [eV]
+
+    # pressure
+    p  = 66.6*1.5      # [J/m^3] *1.5 to convert it to energy (500 mTorr)
+
+    # gas energy at the wall
+    Tg0 = 0.038778    # 3/2*300K*kB ~ (p0 - nT[:,0])/ntot
+
+    # characteristics of driving voltage
+    V0  = inputV0                 # amplitude of driving voltage [V]
+    verticalShift = inputVDC      # DC voltage (vertical shift in driving voltage)
+    tau = (1./13.56e6)             # period of driving voltage [s]
+    L   = 2.00*0.005              # half-gap-width [m] (gap width is 2 cm)
+    electrodeArea = np.pi*0.05**2 # electrode area [m^2] (electrode diameter = 0.1 m)
+
+    # Add voltage uncertainty
+    V0 += h5.File('../../../BOLSIGChemistry_Voltage/Voltage.%08d.h5' % (iSample), 'r')["V_Err"][0]
+
+    # transport parameters
+    nmue = 9.66e21   # argon number density times electron mobility [1/(V*cm*s)]
+    nmum = 0.0
+    nmui = 8.0e19
+    nDe  = 3.86e22   # argon number density times electron diffusivity [1/(cm*s)]
+    nDi  = 2.07e18   # argon number density times ion diffusivity [1/(cm*s)]
+    nDm  = 2.42e18   # argon number density times metastable diffusivity [1/(cm*s)]
+
+    # reaction parameters (NB: k_i = Ck*Ee^B*exp(-A/Ee))
+    #                          Ee = 3/2*Te (Te in eV)
+    #                          -> k_i = [Ck*(2/3)^B] * Ee^B * exp[-(3/2)*A/Ee]
+    # nominal
+    Ck = np.array([2.0e-13,2.1e-15,5.0e-16,6.4e-16,2.1e-21,1.32e8,1.72e7,1.50e7,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0e-18,4.0e-19,0.0,0.0,0.0,0.0,2.5e-17,2.5e-17,1.0e-15,1.0e-15,0.0,0.0]) # pre-exponential factors [m^3/s]
+    B  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-0.5,0,0,0,0,0,0,0,0,0,0])
+    A  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]) # activation temperature [eV]
+    dH = np.array([0.0,-7.541,-10.577,-7.393,0.0,0.0,0.0,0.0,11.577,11.725,13.168,15.76,-11.577,4.183,0.148,1.592,2.592,-1.444,-1.592,-11.725,-0.148,1.444,0.0,0.0,-4.183,-4.035,-2.592,-15.76,0.0,0.0,-8.985,-9.133,4.035,-13.168]) # energy lost per electron due to ionization rxn [eV]
+    dEps = np.array([0.0,15.76,11.577,11.725,13.168,0.0])
+
+    # BC parameters
+    # ks = 1.19e7  # electron recombination rate [cm/s]
+    ks = 1.366109824889323e7 # electron recombination rate [cm/s/eV]
+
+    ###################################################################
+    # Constants of nature (probably shouldn't change unless you have
+    # root privileges on universe)
+    ###################################################################
+    qe   = 1.6e-19   # unit charge [C]
+    eps0 = 8.86e-12  # permittivity of free space [F/m]
+    kB   = 1.38e-23  # Boltzmann constant [J/K]
+    # kB   = 8.62e−5 # Boltzmann constant [eV/K]
+
+
+    ###################################################################
+    # Calculate non-dimensional parameters
+    ###################################################################
+
+    # 1) Convert input units to base SI (except eV)
+    nDe  *= 100. # 1/(m*s)
+    nDi  *= 100. # 1/(m*s)
+    nDm  *= 100.
+    nmue *= 100. # 1/(V*m*s)
+    nmui *= 100. # 1/(V*m*s)
+    ks   *= 0.01 # m/s
+    se   *= 1.0e-20  # m^2
+
+    # 2) Compute "raw" transport parameters
+    De  = nDe/nAr
+    Di  = nDi/nAr
+    Dm  = nDm/nAr
+
+    mue = nmue/nAr
+    mui = nmui/nAr
+    mum = nmum/nAr
+
+    # 3) Compute non-dimensional properties required by solver
+    De    = De*tau/(L*L)
+    Di    = Di*tau/(L*L)
+    Dm    = Dm*tau/(L*L)
+
+    mue   = mue*V0*tau/(L*L)
+    mui   = mui*V0*tau/(L*L)
+    mum   = mum*V0*tau/(L*L)
+
+    Ck[0:4]  *= tau*np0
+    Ck[4]    *= tau*nAr
+    Ck[5:8]  *= tau
+    Ck[8:12] *= tau*nAr
+    Ck[12:24]  *= tau*np0
+    Ck[24:28] *= tau*np0*np0
+    Ck[28:30] *= tau*nAr
+    Ck[30:] *= tau*np0
+    A       = A*1.5/e0  # 1.5 to convert from temperature to energy
+    dH      = dH/e0
+    qStar   = V0/e0 # qe*V0/e0, since e0 in eV, need qe*V0 in eV, which is just V0 in V
+    alpha   = qe*np0*L*L/(V0*eps0)
+    ks      = ks*tau/L
+    p0      = p/qe/np0
+    kappaB  = 4.878171165833662*1.6129 # non-dimensional thermal conductivity of background specie
+                                # (2/3)*tau/L**2*Kb/np0/kB,
+                                # where Kb is the thermal conductivity of background specie
+
+    params.beta = np.array([[0,1,1,1,0,0,0,0,1,1,1,2,1,2,1,1,2,1,1,1,1,1,0,0,1,1,1,1,0,0,1,1,2,1],                     # E
+                            [0,1,1,1,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0],                     # AR+
+                            [0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,1,0,0,0,0,0],                     # AR(m)
+                            [0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0],                     # AR(r)
+                            [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0],                     # AR(4p)
+                            [2,1,1,1,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1]], dtype=np.int64)    # AR
+
+    params.alfa = np.array([[0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,0,0,0,0,1,1],                     # E
+                            [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0],                     # AR+
+                            [2,1,0,2,1,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],                     # AR(m)
+                            [0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,1,0],                     # AR(r)
+                            [0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,0,1],                     # AR(4p)
+                            [0,0,0,0,1,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0]], dtype=np.int64)    # AR
+    
+    # Rxn1:  2AR(m)         ->   2AR
+    # Rxn2:  AR(m) + AR(r)  ->   E + AR+ + AR
+    # Rxn3:  2AR(4p)        ->   E + AR+ + AR
+    # Rxn4:  2AR(m)         ->   E + AR+ + AR
+    # Rxn5:  AR(m) + AR     ->   2AR
+    # Rxn6:  AR(r)          ->   AR + hv
+    # Rxn7:  AR(4p)         ->   AR(m) + hv
+    # Rxn8:  AR(4p)         ->   AR(r) + hv
+    # Rxn9:  E + AR         ->   E + AR(m)
+    # Rxn10: E + AR         ->   E + AR(r)
+    # Rxn11: E + AR         ->   E + AR(4p)
+    # Rxn12: E + AR         ->   2E + AR+
+    # Rxn13: E + AR(m)      ->   E + AR
+    # Rxn14: E + AR(m)      ->   2E + AR+
+    # Rxn15: E + AR(m)      ->   E + AR(r)
+    # Rxn16: E + AR(m)      ->   E + AR(4p)
+    # Rxn17: E + AR(4p)     ->   2E + AR+
+    # Rxn18: E + AR(4p)     ->   E + AR(r)
+    # Rxn19: E + AR(4p)     ->   E + AR(m)
+    # Rxn20: E + AR(r)      ->   E + AR
+    # Rxn21: E + AR(r)      ->   E + AR(m)
+    # Rxn22: E + AR(r)      ->   E + AR(4p)
+    # Rxn23: E + AR+        ->   AR(m) + hv
+    # Rxn24: E + AR+        ->   AR(4p) + hv
+    # Rxn25: 2E + AR+       ->   E + AR(m)
+    # Rxn26: 2E + AR+       ->   E + AR(r)
+    # Rxn27: 2E + AR+       ->   E + AR(4p)
+    # Rxn28: 2E + AR+       ->   E + AR
+    # Rxn29: AR + AR(4p)    ->   AR + AR(m)
+    # Rxn30: AR + AR(4p)    ->   AR + AR(r)
+    # Rxn31: AR(m) + AR(4p) ->   E + AR+ + AR
+    # Rxn32: AR(r) + AR(4p) ->   E + AR+ + AR
+    # Rxn33: E + AR(r)      ->   2E + AR+
+    # Rxn34: E + AR(4p)     ->   E + AR 
+
+    rxnNameDict = {0: "Ar(m) + Ar(m) => Ar + Ar",
+                   1: "Ar(m) + Ar(r) => E + Ar + Ar+",
+                   2: "Ar(4p) + Ar(4p) => E + Ar + Ar+",
+                   3: "Ar(m) + Ar(m) => E + Ar + Ar+",
+                   4: "Ar + Ar(m) => Ar + Ar",
+                   5: "Ar(r) => Ar",
+                   6: "Ar(4p) => Ar(m)",
+                   7: "Ar(4p) => Ar(r)",
+                   8: "Excitation_Metastable",
+                   9: "Excitation_Resonant",
+                   10: "Excitation_4p",
+                   11: "Ionization",
+                   12: "DeExcitation_Metastable",
+                   13: "StepIonization_Metastable",
+                   14: "E + Ar(m) => E + Ar(r)",
+                   15: "E + Ar(m) => E + Ar(4p)",
+                   16: "StepIonization_4p",
+                   17: "E + Ar(4p) => E + Ar(r)",
+                   18: "E + Ar(4p) => E + Ar(m)",
+                   19: "DeExcitation_Resonant",
+                   20: "E + Ar(r) => E + Ar(m)",
+                   21: "E + Ar(r) => E + Ar(4p)",
+                   22: "E + Ar+ => Ar(m)",
+                   23: "E + Ar+ => Ar(4p)",
+                   24: "3BdyRecomb_Metastable",
+                   25: "3BdyRecomb_Resonant",
+                   26: "3BdyRecomb_4p",
+                   27: "3BdyRecomb_Ground",
+                   28: "Ar + Ar(4p) => Ar + Ar(m)",
+                   29: "Ar + Ar(4p) => Ar + Ar(r)",
+                   30: "Ar(m) + Ar(4p) => E + Ar+ + Ar",
+                   31: "Ar(r) + Ar(4p) => E + Ar+ + Ar",
+                   32: "StepIonization_Resonant",
+                   33: "DeExcitation_4p"}
+
+    # 4) Set values in params class
+    params.D[0]    = De
+    params.D[1]    = Di
+    params.D[2]    = Dm
+
+    params.mu[0]   = mue
+    params.mu[1]   = mui
+    params.mu[2]   = mum
+
+    params.A[:]    = Ck[:]
+    params.B[:]    = B[:]
+    params.C[:]    = A[:]
+
+    # Account for the 2/3 term to convert from electron temperature to electron energy
+    for i in range(len(params.A)):
+        params.A[i] *= (2/3)**(params.B[i])
+
+    params.dH[:]   = dH[:]
+    params.dEps[:] = dEps[:]
+    params.qStar   = qStar
+    params.alpha   = alpha
+    params.ks      = ks
+    params.gam     = gam
+    params.kappaB  = kappaB
+    params.nAronp0 = nAr / np0
+    params.p0      = p0
+    params.Tg0     = Tg0
+    params.EC      = 2.0 * me / mAr \
+        * np.sqrt(16.0 * (me + mAr) * e0 * c**2
+                  / (3.0 * np.pi * me * mAr)) * se * nAr * tau
+    # params.EC = 2.0 * me / mAr * 3.8e9 * tau
+
+    params.verticalShift = verticalShift / V0
+
+    # Parameters needed to compute the current with dimensions
+    params.V0Ltau  = V0 / (L * tau)
+    params.V0L     = V0 / L
+    params.LLV0tau = (L*L) / (V0*tau)
+    params.tauL    = L / tau
+    params.np0     = np0           # "nominal" electron density [1/m^3]
+    params.qe      = qe            # unit charge [C]
+    params.eps0    = eps0          # unit charge [C]
+    params.eArea   = electrodeArea # electrode area [m^2]
+
+
+    reactionExpressionTypelist =  np.array([False,False,False,False,False,False,False,False,
+                                           True,True,True,True,True,True,True,True,True,True,True,True,True,True,
+                                           False,False,True,True,True,True,False,False,False,False,True,True])
+
+    thresholded_rxn = np.array([False, False, False, False, False, False, False, False,
+                                True, True, True, True, False, True, True, True, True, False, False, False, False,
+                                True, False, False, False, False, False, False, False, False, False, False, True,
+                                False])
+    reactionsList = []
+    LOGFilename = 'interpolationSample%s.log'%str(iSample)
+    f = open(LOGFilename, 'w')
+
+    for i in range(Nr):
+        sample_root_dir = "../../../BOLSIGChemistry_6SpeciesRates"
+        if reactionExpressionTypelist[i]:
+            if i < 14 or i == 16 or i == 19 or i > 23:
+                fileString = sample_root_dir + "/" + rxnNameDict[i]
+                fileName = "%s.%08d.h5" % (fileString, iSample)
+                f = h5.File(fileName, 'r')
+                dataset = f["table"]
+            else:
+                fileString = sample_root_dir + "/" + "StepwiseExcitations"
+                fileName = "%s.%08d.h5" % (fileString, iSample)
+                f = h5.File(fileName, 'r')
+                dataset = f[rxnNameDict[i]]
+            
+            rateCoeff = dataset[:,1]
+            if i > 23 and i < 28:
+                rateCoeff /= 6.022e23**2
+            else:
+                rateCoeff /= 6.022e23
+            Te = dataset[:,0]
+            Te /= 11604.
+
+            ## Removing BOLSIG failures
+            fail_inds = []
+            for j in range(len(rateCoeff)):
+                if rateCoeff[j] == 0.0 and j > np.nonzero(rateCoeff)[0][0]:
+                    fail_inds.append(j)
+
+            Te = np.delete(Te, fail_inds)
+            rateCoeff = np.delete(rateCoeff, fail_inds)
+
+            # Sorting mean energy array and rate coefficient array based on
+            # the mean energy array.
+            Teinds = Te.argsort()
+            rateCoeff = rateCoeff[Teinds]
+            Te = Te[Teinds]
+
+            # Find duplicates
+            TeDuplicateinds = np.where(np.abs(np.diff(Te, axis=0)) > 0.0)
+            TeDuplicateindsForLog = np.where(np.abs(np.diff(Te, axis=0)) == 0.0)
+            rateCoeff = rateCoeff[TeDuplicateinds]
+            Te = Te[TeDuplicateinds]
+
+            # Nondimensionalization of mean energy.
+            Te *= 1.5
+
+            # Find first non-zero value of the coefficient rate.
+            I = np.nonzero(rateCoeff)
+
+            diffRateCoeff = [j-i for i, j in zip(rateCoeff[:-1], rateCoeff[1:])]
+            diffTe = [j-i for i, j in zip(Te[:-1], Te[1:])]
+
+            Monotonicity = np.asarray([j/i for i, j in zip(diffTe, diffRateCoeff)])
+            Monotonicity = np.insert(Monotonicity, 0, 0.0, axis=0)
+
+            Nan = np.isnan(Monotonicity)
+            Inf = np.isinf(Monotonicity)
+            if thresholded_rxn[i] == True:
+                indexPositive = np.where(Monotonicity>0.0)
+            else:
+                indexPositive = np.where(Monotonicity<0.0)
+            Positive = np.full(Monotonicity.shape, False, dtype=bool)
+            Positive[indexPositive] = True
+
+            indices = Nan + Inf + Positive
+
+            #lastFalse = np.where(indices==False)[-1][-1] + 2
+            for k in range(len(Te)):
+                if (Te[k] < 4.5 and indices[k] == False):
+                    lastFalse = k + 2
+
+            # Transformation to log scale.
+            TeLog = np.log(Te)
+
+            # Compute the slope of the rate coefficient between its first two non-zero values.
+            # Finite differences are used.
+            dydx = (rateCoeff[lastFalse + 1] - rateCoeff[lastFalse]) \
+                 / (Te[lastFalse + 1] - Te[lastFalse])
+
+            # Arrhenius form: kf = A * exp(-C / Te)
+            C = Te[lastFalse]**2.0*dydx / rateCoeff[lastFalse]
+
+            # Compute pre-exponential coefficient, A, in log scale.
+            ALog = np.log(rateCoeff[lastFalse]) + C / Te[lastFalse]
+
+            # Transform rate coefficient in log scale.
+            rateCoeffLog = np.zeros(rateCoeff.shape)
+            rateCoeffLog[lastFalse:] = np.log(rateCoeff[lastFalse:])
+            # For the troublesome values, we use the Arrhenius form.
+            rateCoeffLog[0:lastFalse] = ALog - C / Te[0:lastFalse]
+            # Nondimensionalization in log scale.
+            if i < 12:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
+            elif i > 23 and i < 28:
+                rateCoeffLog += - np.log(1.0/tau) + 2*np.log(np0)
+            else:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(np0)
+
+            # Interpolation in log scale.
+            reactionExpressionsLog = CubicSpline(TeLog, rateCoeffLog)
+            # Gradient in log scale
+            reactionTExpressionsLog = CubicSpline.derivative(reactionExpressionsLog)
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf_log = reactionExpressionsLog,
+                                kf_T_log = reactionTExpressionsLog)
+            reactionsList.append(reaction)
+
+            logging.basicConfig(filename=LOGFilename)
+            logging.warning('Interpolation info (Reaction %s):', i)
+            logging.warning('First non-zero entry in the rate coefficient: %s', I[0][0])
+            logging.warning('Monotonicity of the rate coefficient start from entry: %s', lastFalse)
+            logging.warning('Position of possible duplicates in mean energy array: %s',
+                          TeDuplicateindsForLog[0])
+
+        else:
+            fileName = "%s/Arrhenius.%08d.h5" % (sample_root_dir, iSample)
+            f = h5.File(fileName, 'r')
+            arrh_Coeffs = f[rxnNameDict[i]][...]
+            A, B, C = arrh_Coeffs
+
+            # Non-dimensionalize the coefficients
+            if i < 4:
+                A /= 6.022e23
+                A *= tau*np0
+            elif i == 4:
+                A /= 6.022e23
+                A *= tau*nAr
+            elif i > 4 and i < 8:
+                A *= tau
+            elif i > 27 and i < 30:
+                A /= 6.022e23
+                A *= tau*nAr
+            else:
+                A /= 6.022e23
+                A *= tau*np0
+
+            A *= ((2./3.)*11604)**B 
+            C *= 1.5/(e0*11604)
+
+            rxn = eval("lambda energy :" + f"{A} * energy**{B} * np.exp(-{C} / energy)")
+            rxn_T = eval("lambda energy :" + f"{A} * energy**({B}-1) * np.exp(-{C} / energy) * ({B} + {C} / energy)")
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf = rxn, kf_T = rxn_T)
+            reactionsList.append(reaction)
+
+    params.reactionsList = reactionsList
+
+    diffList = []
+    # Data from BOLSIG
+    # Te in [eV]
+    # De * N in [1/(m*s)]
+    transport = h5.File("../../../BOLSIGChemistry_6SpeciesRates/Transport.%08d.h5" % (iSample), 'r')
+    NDe_v_Te = transport["diffusivity"]
+    Te_trans = NDe_v_Te[:,0]
+    Te_trans /= 11604
+    De_interp = (NDe_v_Te[:,1]/nAr)*tau/(L*L)
+    De_spline = CubicSpline(Te_trans, De_interp)
+    De_Te_spline = CubicSpline.derivative(De_spline)
+    diffusivity = Diffusivity(interpolate = True, D_expression = De_spline, D_T_expression = De_Te_spline)
+    diffList.append(diffusivity)
+
+    Ns = 6
+    for i in range(1, Ns):
+        diffList.append(Diffusivity(interpolate = False))
+
+    params.diffusivityList = diffList
+
+    muList = []
+    # Data from BOLSIG
+    # Te in [eV]
+    # Mue * N in [1/(V*m*s)]
+    Nmue_v_Te = transport["mobility"]
+    mue_interp = (Nmue_v_Te[:,1]/nAr)*V0*tau/(L*L)
+    mue_spline = CubicSpline(Te_trans, mue_interp)
+    mue_Te_spline = CubicSpline.derivative(mue_spline)
+    mobility = Mobility(interpolate = True, mu_expression = mue_spline, mu_T_expression = mue_Te_spline)
+    muList.append(mobility)
+
+    Ns = 6
+    for i in range(1, Ns):
+        muList.append(Mobility(interpolate = False))
+
+    params.mobilityList = muList
+
+    # 5) Dump to screen
+    params.print()
diff --git a/psaapProperties_6Species_Sampling_5Torr_Expanded.py b/psaapProperties_6Species_Sampling_5Torr_Expanded.py
new file mode 100755
index 000000000..6c9f7b16a
--- /dev/null
+++ b/psaapProperties_6Species_Sampling_5Torr_Expanded.py
@@ -0,0 +1,494 @@
+import numpy as np
+from scipy.interpolate import CubicSpline
+import csv
+import matplotlib.pyplot as plt
+import matplotlib.colors as mcolors
+import h5py as h5
+import logging
+
+class Reaction(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Diffusivity(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+class Mobility(object):
+    def __init__(self, *initial_data, **kwargs):
+        for dictionary in initial_data:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+
+
+def setPsaapProperties_6Species_Sampling_5Torr_Expanded(gam, inputV0, inputVDC, params, Nr, iSample):
+    """Sets non-dimensional properties corresponding to Liu 2014 paper.
+
+    Inputs:
+      gam : Secondary electron emission coefficient
+      params : chebSolver.modelParams class
+
+    Outputs: None
+      params data is overwritten using values from Liu 2014.
+    """
+    ###################################################################
+    # User specified parameters (you may change these if you wish to
+    # run a different scenario from Liu 2014)
+    ###################################################################
+
+    # densities
+    nAr = 1.61e23     # background number density of Ar [1/m^3] (corresponds to p = 5 Torr)
+    np0 = 8e16        # "nominal" electron density [1/m^3]
+
+    # masses
+    # me = 9.10938356e-31        # mass of an electron [kg]
+    # me = 5.489e-4              # mass of an electron [u]
+    me = 0.511e6                 # mass of an electron [eV/c2]
+    # mAr = 39.948               # mass of an argon atom [u]
+    # mAr = 39.948 * 1.66054e-27 # mass of an argon atom [kg]
+    mAr = 37.2158e9              # mass of an electron [eV/c2]
+    # u = 931.4941e6             # eV/c2
+    c = 299792458                # speed of light [m/s]
+    se = 40                      # momentum cross section [A^2]
+
+    # nominal electron energy
+    e0 = 1.0  # [eV]
+
+    # pressure
+    p  = 666.6*1.5      # [J/m^3] *1.5 to convert it to energy (5 Torr)
+
+    # gas energy at the wall
+    Tg0 = 0.038778    # 3/2*300K*kB ~ (p0 - nT[:,0])/ntot
+
+    # characteristics of driving voltage
+    V0  = inputV0                 # amplitude of driving voltage [V]
+    verticalShift = inputVDC      # DC voltage (vertical shift in driving voltage)
+    tau = (1./13.56e6)             # period of driving voltage [s]
+    L   = 2.00*0.005              # half-gap-width [m] (gap width is 2 cm)
+    electrodeArea = np.pi*0.05**2 # electrode area [m^2] (electrode diameter = 0.1 m)
+
+    # Add voltage uncertainty
+    V0 += h5.File('../../../BOLSIGChemistry_Voltage/Voltage.%08d.h5' % (iSample), 'r')["V_Err"][0]
+
+    # transport parameters
+    nmue = 9.66e21   # argon number density times electron mobility [1/(V*cm*s)]
+    nmum = 0.0
+    nmui = 8.0e19
+    nDe  = 3.86e22   # argon number density times electron diffusivity [1/(cm*s)]
+    nDi  = 2.07e18   # argon number density times ion diffusivity [1/(cm*s)]
+    nDm  = 2.42e18   # argon number density times metastable diffusivity [1/(cm*s)]
+
+    # reaction parameters (NB: k_i = Ck*Ee^B*exp(-A/Ee))
+    #                          Ee = 3/2*Te (Te in eV)
+    #                          -> k_i = [Ck*(2/3)^B] * Ee^B * exp[-(3/2)*A/Ee]
+    # nominal
+    Ck = np.array([2.0e-13,2.1e-15,5.0e-16,6.4e-16,2.1e-21,1.32e8,1.72e7,1.50e7,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0e-18,4.0e-19,0.0,0.0,0.0,0.0,2.5e-17,2.5e-17,1.0e-15,1.0e-15,0.0,0.0]) # pre-exponential factors [m^3/s]
+    B  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-0.5,0,0,0,0,0,0,0,0,0,0])
+    A  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]) # activation temperature [eV]
+    dH = np.array([0.0,-7.541,-10.577,-7.393,0.0,0.0,0.0,0.0,11.577,11.725,13.168,15.76,-11.577,4.183,0.148,1.592,2.592,-1.444,-1.592,-11.725,-0.148,1.444,0.0,0.0,-4.183,-4.035,-2.592,-15.76,0.0,0.0,-8.985,-9.133,4.035,-13.168]) # energy lost per electron due to ionization rxn [eV]
+    dEps = np.array([0.0,15.76,11.577,11.725,13.168,0.0])
+
+    # BC parameters
+    # ks = 1.19e7  # electron recombination rate [cm/s]
+    ks = 1.366109824889323e7 # electron recombination rate [cm/s/eV]
+
+    ###################################################################
+    # Constants of nature (probably shouldn't change unless you have
+    # root privileges on universe)
+    ###################################################################
+    qe   = 1.6e-19   # unit charge [C]
+    eps0 = 8.86e-12  # permittivity of free space [F/m]
+    kB   = 1.38e-23  # Boltzmann constant [J/K]
+    # kB   = 8.62e−5 # Boltzmann constant [eV/K]
+
+
+    ###################################################################
+    # Calculate non-dimensional parameters
+    ###################################################################
+
+    # 1) Convert input units to base SI (except eV)
+    nDe  *= 100. # 1/(m*s)
+    nDi  *= 100. # 1/(m*s)
+    nDm  *= 100.
+    nmue *= 100. # 1/(V*m*s)
+    nmui *= 100. # 1/(V*m*s)
+    ks   *= 0.01 # m/s
+    se   *= 1.0e-20  # m^2
+
+    # 2) Compute "raw" transport parameters
+    De  = nDe/nAr
+    Di  = nDi/nAr
+    Dm  = nDm/nAr
+
+    mue = nmue/nAr
+    mui = nmui/nAr
+    mum = nmum/nAr
+
+    # 3) Compute non-dimensional properties required by solver
+    De    = De*tau/(L*L)
+    Di    = Di*tau/(L*L)
+    Dm    = Dm*tau/(L*L)
+
+    mue   = mue*V0*tau/(L*L)
+    mui   = mui*V0*tau/(L*L)
+    mum   = mum*V0*tau/(L*L)
+
+    Ck[0:4]  *= tau*np0
+    Ck[4]    *= tau*nAr
+    Ck[5:8]  *= tau
+    Ck[8:12] *= tau*nAr
+    Ck[12:24]  *= tau*np0
+    Ck[24:28] *= tau*np0*np0
+    Ck[28:30] *= tau*nAr
+    Ck[30:] *= tau*np0
+    A       = A*1.5/e0  # 1.5 to convert from temperature to energy
+    dH      = dH/e0
+    qStar   = V0/e0 # qe*V0/e0, since e0 in eV, need qe*V0 in eV, which is just V0 in V
+    alpha   = qe*np0*L*L/(V0*eps0)
+    ks      = ks*tau/L
+    p0      = p/qe/np0
+    kappaB  = 4.878171165833662*1.6129 # non-dimensional thermal conductivity of background specie
+                                # (2/3)*tau/L**2*Kb/np0/kB,
+                                # where Kb is the thermal conductivity of background specie
+
+    params.beta = np.array([[0,1,1,1,0,0,0,0,1,1,1,2,1,2,1,1,2,1,1,1,1,1,0,0,1,1,1,1,0,0,1,1,2,1],                     # E
+                            [0,1,1,1,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0],                     # AR+
+                            [0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,1,0,0,0,0,0],                     # AR(m)
+                            [0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0],                     # AR(r)
+                            [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0],                     # AR(4p)
+                            [2,1,1,1,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1]], dtype=np.int64)    # AR
+
+    params.alfa = np.array([[0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,0,0,0,0,1,1],                     # E
+                            [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0],                     # AR+
+                            [2,1,0,2,1,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],                     # AR(m)
+                            [0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,1,0],                     # AR(r)
+                            [0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,0,1],                     # AR(4p)
+                            [0,0,0,0,1,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0]], dtype=np.int64)    # AR
+    
+    # Rxn1:  2AR(m)         ->   2AR
+    # Rxn2:  AR(m) + AR(r)  ->   E + AR+ + AR
+    # Rxn3:  2AR(4p)        ->   E + AR+ + AR
+    # Rxn4:  2AR(m)         ->   E + AR+ + AR
+    # Rxn5:  AR(m) + AR     ->   2AR
+    # Rxn6:  AR(r)          ->   AR + hv
+    # Rxn7:  AR(4p)         ->   AR(m) + hv
+    # Rxn8:  AR(4p)         ->   AR(r) + hv
+    # Rxn9:  E + AR         ->   E + AR(m)
+    # Rxn10: E + AR         ->   E + AR(r)
+    # Rxn11: E + AR         ->   E + AR(4p)
+    # Rxn12: E + AR         ->   2E + AR+
+    # Rxn13: E + AR(m)      ->   E + AR
+    # Rxn14: E + AR(m)      ->   2E + AR+
+    # Rxn15: E + AR(m)      ->   E + AR(r)
+    # Rxn16: E + AR(m)      ->   E + AR(4p)
+    # Rxn17: E + AR(4p)     ->   2E + AR+
+    # Rxn18: E + AR(4p)     ->   E + AR(r)
+    # Rxn19: E + AR(4p)     ->   E + AR(m)
+    # Rxn20: E + AR(r)      ->   E + AR
+    # Rxn21: E + AR(r)      ->   E + AR(m)
+    # Rxn22: E + AR(r)      ->   E + AR(4p)
+    # Rxn23: E + AR+        ->   AR(m) + hv
+    # Rxn24: E + AR+        ->   AR(4p) + hv
+    # Rxn25: 2E + AR+       ->   E + AR(m)
+    # Rxn26: 2E + AR+       ->   E + AR(r)
+    # Rxn27: 2E + AR+       ->   E + AR(4p)
+    # Rxn28: 2E + AR+       ->   E + AR
+    # Rxn29: AR + AR(4p)    ->   AR + AR(m)
+    # Rxn30: AR + AR(4p)    ->   AR + AR(r)
+    # Rxn31: AR(m) + AR(4p) ->   E + AR+ + AR
+    # Rxn32: AR(r) + AR(4p) ->   E + AR+ + AR
+    # Rxn33: E + AR(r)      ->   2E + AR+
+    # Rxn34: E + AR(4p)     ->   E + AR 
+
+    rxnNameDict = {0: "Ar(m) + Ar(m) => Ar + Ar",
+                   1: "Ar(m) + Ar(r) => E + Ar + Ar+",
+                   2: "Ar(4p) + Ar(4p) => E + Ar + Ar+",
+                   3: "Ar(m) + Ar(m) => E + Ar + Ar+",
+                   4: "Ar + Ar(m) => Ar + Ar",
+                   5: "Ar(r) => Ar",
+                   6: "Ar(4p) => Ar(m)",
+                   7: "Ar(4p) => Ar(r)",
+                   8: "Excitation_Metastable",
+                   9: "Excitation_Resonant",
+                   10: "Excitation_4p",
+                   11: "Ionization",
+                   12: "DeExcitation_Metastable",
+                   13: "StepIonization_Metastable",
+                   14: "E + Ar(m) => E + Ar(r)",
+                   15: "E + Ar(m) => E + Ar(4p)",
+                   16: "StepIonization_4p",
+                   17: "E + Ar(4p) => E + Ar(r)",
+                   18: "E + Ar(4p) => E + Ar(m)",
+                   19: "DeExcitation_Resonant",
+                   20: "E + Ar(r) => E + Ar(m)",
+                   21: "E + Ar(r) => E + Ar(4p)",
+                   22: "E + Ar+ => Ar(m)",
+                   23: "E + Ar+ => Ar(4p)",
+                   24: "3BdyRecomb_Metastable",
+                   25: "3BdyRecomb_Resonant",
+                   26: "3BdyRecomb_4p",
+                   27: "3BdyRecomb_Ground",
+                   28: "Ar + Ar(4p) => Ar + Ar(m)",
+                   29: "Ar + Ar(4p) => Ar + Ar(r)",
+                   30: "Ar(m) + Ar(4p) => E + Ar+ + Ar",
+                   31: "Ar(r) + Ar(4p) => E + Ar+ + Ar",
+                   32: "StepIonization_Resonant",
+                   33: "DeExcitation_4p"}
+
+    # 4) Set values in params class
+    params.D[0]    = De
+    params.D[1]    = Di
+    params.D[2]    = Dm
+
+    params.mu[0]   = mue
+    params.mu[1]   = mui
+    params.mu[2]   = mum
+
+    params.A[:]    = Ck[:]
+    params.B[:]    = B[:]
+    params.C[:]    = A[:]
+
+    # Account for the 2/3 term to convert from electron temperature to electron energy
+    for i in range(len(params.A)):
+        params.A[i] *= (2/3)**(params.B[i])
+
+    params.dH[:]   = dH[:]
+    params.dEps[:] = dEps[:]
+    params.qStar   = qStar
+    params.alpha   = alpha
+    params.ks      = ks
+    params.gam     = gam
+    params.kappaB  = kappaB
+    params.nAronp0 = nAr / np0
+    params.p0      = p0
+    params.Tg0     = Tg0
+    params.EC      = 2.0 * me / mAr \
+        * np.sqrt(16.0 * (me + mAr) * e0 * c**2
+                  / (3.0 * np.pi * me * mAr)) * se * nAr * tau
+    # params.EC = 2.0 * me / mAr * 3.8e9 * tau
+
+    params.verticalShift = verticalShift / V0
+
+    # Parameters needed to compute the current with dimensions
+    params.V0Ltau  = V0 / (L * tau)
+    params.V0L     = V0 / L
+    params.LLV0tau = (L*L) / (V0*tau)
+    params.tauL    = L / tau
+    params.np0     = np0           # "nominal" electron density [1/m^3]
+    params.qe      = qe            # unit charge [C]
+    params.eps0    = eps0          # unit charge [C]
+    params.eArea   = electrodeArea # electrode area [m^2]
+
+
+    reactionExpressionTypelist =  np.array([False,False,False,False,False,False,False,False,
+                                           True,True,True,True,True,True,True,True,True,True,True,True,True,True,
+                                           False,False,True,True,True,True,False,False,False,False,True,True])
+
+    thresholded_rxn = np.array([False, False, False, False, False, False, False, False,
+                                True, True, True, True, False, True, True, True, True, False, False, False, False,
+                                True, False, False, False, False, False, False, False, False, False, False, True,
+                                False])
+    reactionsList = []
+    LOGFilename = 'interpolationSample%s.log'%str(iSample)
+    f = open(LOGFilename, 'w')
+
+    for i in range(Nr):
+        sample_root_dir = "../../../BOLSIGChemistry_6SpeciesRates"
+        if reactionExpressionTypelist[i]:
+            if i < 14 or i == 16 or i == 19 or i > 23:
+                fileString = sample_root_dir + "/" + rxnNameDict[i]
+                fileName = "%s.%08d.h5" % (fileString, iSample)
+                f = h5.File(fileName, 'r')
+                dataset = f["table"]
+            else:
+                fileString = sample_root_dir + "/" + "StepwiseExcitations"
+                fileName = "%s.%08d.h5" % (fileString, iSample)
+                f = h5.File(fileName, 'r')
+                dataset = f[rxnNameDict[i]]
+            
+            rateCoeff = dataset[:,1]
+            if i > 23 and i < 28:
+                rateCoeff /= 6.022e23**2
+            else:
+                rateCoeff /= 6.022e23
+            Te = dataset[:,0]
+            Te /= 11604.
+
+            ## Removing BOLSIG failures
+            fail_inds = []
+            for j in range(len(rateCoeff)):
+                if rateCoeff[j] == 0.0 and j > np.nonzero(rateCoeff)[0][0]:
+                    fail_inds.append(j)
+
+            Te = np.delete(Te, fail_inds)
+            rateCoeff = np.delete(rateCoeff, fail_inds)
+
+            # Sorting mean energy array and rate coefficient array based on
+            # the mean energy array.
+            Teinds = Te.argsort()
+            rateCoeff = rateCoeff[Teinds]
+            Te = Te[Teinds]
+
+            # Find duplicates
+            TeDuplicateinds = np.where(np.abs(np.diff(Te, axis=0)) > 0.0)
+            TeDuplicateindsForLog = np.where(np.abs(np.diff(Te, axis=0)) == 0.0)
+            rateCoeff = rateCoeff[TeDuplicateinds]
+            Te = Te[TeDuplicateinds]
+
+            # Nondimensionalization of mean energy.
+            Te *= 1.5
+
+            # Find first non-zero value of the coefficient rate.
+            I = np.nonzero(rateCoeff)
+
+            diffRateCoeff = [j-i for i, j in zip(rateCoeff[:-1], rateCoeff[1:])]
+            diffTe = [j-i for i, j in zip(Te[:-1], Te[1:])]
+
+            Monotonicity = np.asarray([j/i for i, j in zip(diffTe, diffRateCoeff)])
+            Monotonicity = np.insert(Monotonicity, 0, 0.0, axis=0)
+
+            Nan = np.isnan(Monotonicity)
+            Inf = np.isinf(Monotonicity)
+            if thresholded_rxn[i] == True:
+                indexPositive = np.where(Monotonicity>0.0)
+            else:
+                indexPositive = np.where(Monotonicity<0.0)
+            Positive = np.full(Monotonicity.shape, False, dtype=bool)
+            Positive[indexPositive] = True
+
+            indices = Nan + Inf + Positive
+
+            #lastFalse = np.where(indices==False)[-1][-1] + 2
+            for k in range(len(Te)):
+                if (Te[k] < 4.5 and indices[k] == False):
+                    lastFalse = k + 2
+
+            # Transformation to log scale.
+            TeLog = np.log(Te)
+
+            # Compute the slope of the rate coefficient between its first two non-zero values.
+            # Finite differences are used.
+            dydx = (rateCoeff[lastFalse + 1] - rateCoeff[lastFalse]) \
+                 / (Te[lastFalse + 1] - Te[lastFalse])
+
+            # Arrhenius form: kf = A * exp(-C / Te)
+            C = Te[lastFalse]**2.0*dydx / rateCoeff[lastFalse]
+
+            # Compute pre-exponential coefficient, A, in log scale.
+            ALog = np.log(rateCoeff[lastFalse]) + C / Te[lastFalse]
+
+            # Transform rate coefficient in log scale.
+            rateCoeffLog = np.zeros(rateCoeff.shape)
+            rateCoeffLog[lastFalse:] = np.log(rateCoeff[lastFalse:])
+            # For the troublesome values, we use the Arrhenius form.
+            rateCoeffLog[0:lastFalse] = ALog - C / Te[0:lastFalse]
+            # Nondimensionalization in log scale.
+            if i < 12:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
+            elif i > 23 and i < 28:
+                rateCoeffLog += - np.log(1.0/tau) + 2*np.log(np0)
+            else:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(np0)
+
+            # Interpolation in log scale.
+            reactionExpressionsLog = CubicSpline(TeLog, rateCoeffLog)
+            # Gradient in log scale
+            reactionTExpressionsLog = CubicSpline.derivative(reactionExpressionsLog)
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf_log = reactionExpressionsLog,
+                                kf_T_log = reactionTExpressionsLog)
+            reactionsList.append(reaction)
+
+            logging.basicConfig(filename=LOGFilename)
+            logging.warning('Interpolation info (Reaction %s):', i)
+            logging.warning('First non-zero entry in the rate coefficient: %s', I[0][0])
+            logging.warning('Monotonicity of the rate coefficient start from entry: %s', lastFalse)
+            logging.warning('Position of possible duplicates in mean energy array: %s',
+                          TeDuplicateindsForLog[0])
+
+        else:
+            fileName = "%s/Arrhenius.%08d.h5" % (sample_root_dir, iSample)
+            f = h5.File(fileName, 'r')
+            arrh_Coeffs = f[rxnNameDict[i]][...]
+            A, B, C = arrh_Coeffs
+
+            # Non-dimensionalize the coefficients
+            if i < 4:
+                A /= 6.022e23
+                A *= tau*np0
+            elif i == 4:
+                A /= 6.022e23
+                A *= tau*nAr
+            elif i > 4 and i < 8:
+                A *= tau
+            elif i > 27 and i < 30:
+                A /= 6.022e23
+                A *= tau*nAr
+            else:
+                A /= 6.022e23
+                A *= tau*np0
+
+            A *= ((2./3.)*11604)**B 
+            C *= 1.5/(e0*11604)
+
+            rxn = eval("lambda energy :" + f"{A} * energy**{B} * np.exp(-{C} / energy)")
+            rxn_T = eval("lambda energy :" + f"{A} * energy**({B}-1) * np.exp(-{C} / energy) * ({B} + {C} / energy)")
+
+            reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
+                                rxnBolsig = reactionExpressionTypelist[i],
+                                kf = rxn, kf_T = rxn_T)
+            reactionsList.append(reaction)
+
+    params.reactionsList = reactionsList
+
+    diffList = []
+    # Data from BOLSIG
+    # Te in [eV]
+    # De * N in [1/(m*s)]
+    transport = h5.File("../../../BOLSIGChemistry_6SpeciesRates/Transport.%08d.h5" % (iSample), 'r')
+    NDe_v_Te = transport["diffusivity"]
+    Te_trans = NDe_v_Te[:,0]
+    Te_trans /= 11604
+    De_interp = (NDe_v_Te[:,1]/nAr)*tau/(L*L)
+    De_spline = CubicSpline(Te_trans, De_interp)
+    De_Te_spline = CubicSpline.derivative(De_spline)
+    diffusivity = Diffusivity(interpolate = True, D_expression = De_spline, D_T_expression = De_Te_spline)
+    diffList.append(diffusivity)
+
+    Ns = 6
+    for i in range(1, Ns):
+        diffList.append(Diffusivity(interpolate = False))
+
+    params.diffusivityList = diffList
+
+    muList = []
+    # Data from BOLSIG
+    # Te in [eV]
+    # Mue * N in [1/(V*m*s)]
+    Nmue_v_Te = transport["mobility"]
+    mue_interp = (Nmue_v_Te[:,1]/nAr)*V0*tau/(L*L)
+    mue_spline = CubicSpline(Te_trans, mue_interp)
+    mue_Te_spline = CubicSpline.derivative(mue_spline)
+    mobility = Mobility(interpolate = True, mu_expression = mue_spline, mu_T_expression = mue_Te_spline)
+    muList.append(mobility)
+
+    Ns = 6
+    for i in range(1, Ns):
+        muList.append(Mobility(interpolate = False))
+
+    params.mobilityList = muList
+
+    # 5) Dump to screen
+    params.print()
diff --git a/psaapPropertiesTestArmVioleta.py b/psaapProperties_6Species_Sampling_Expanded_NewRates.py
old mode 100644
new mode 100755
similarity index 50%
rename from psaapPropertiesTestArmVioleta.py
rename to psaapProperties_6Species_Sampling_Expanded_NewRates.py
index 9c3d55670..3b61bfab1
--- a/psaapPropertiesTestArmVioleta.py
+++ b/psaapProperties_6Species_Sampling_Expanded_NewRates.py
@@ -1,8 +1,9 @@
 import numpy as np
 from scipy.interpolate import CubicSpline
-
+import csv
 import matplotlib.pyplot as plt
 import matplotlib.colors as mcolors
+import h5py as h5
 import logging
 
 class Reaction(object):
@@ -29,7 +30,8 @@ def __init__(self, *initial_data, **kwargs):
         for key in kwargs:
             setattr(self, key, kwargs[key])
 
-def setPsaapPropertiesTestArm(gam, inputV0, inputVDC, params, Nr, iSample):
+
+def setPsaapProperties_6Species_Sampling_Expanded_NewRates(gam, inputV0, inputVDC, params, Nr, iSample):
     """Sets non-dimensional properties corresponding to Liu 2014 paper.
 
     Inputs:
@@ -57,7 +59,7 @@ def setPsaapPropertiesTestArm(gam, inputV0, inputVDC, params, Nr, iSample):
     mAr = 37.2158e9              # mass of an electron [eV/c2]
     # u = 931.4941e6             # eV/c2
     c = 299792458                # speed of light [m/s]
-    se = 40                      # momentum cross section [m^2]
+    se = 40                      # momentum cross section [A^2]
 
     # nominal electron energy
     e0 = 1.0  # [eV]
@@ -71,49 +73,27 @@ def setPsaapPropertiesTestArm(gam, inputV0, inputVDC, params, Nr, iSample):
     # characteristics of driving voltage
     V0  = inputV0                 # amplitude of driving voltage [V]
     verticalShift = inputVDC      # DC voltage (vertical shift in driving voltage)
-    tau = (1./13.6e6)             # period of driving voltage [s]
-    L   = 2.00*0.005              # half-gap-width [m] (gap width is 2.54cm)
+    tau = (1./13.56e6)             # period of driving voltage [s]
+    L   = 2.00*0.005              # half-gap-width [m] (gap width is 2 cm)
     electrodeArea = np.pi*0.05**2 # electrode area [m^2] (electrode diameter = 0.1 m)
 
     # transport parameters
     nmue = 9.66e21   # argon number density times electron mobility [1/(V*cm*s)]
-    nmui = 4.65e19   # argon number density times ion mobility [1/(V*cm*s)]
-    nmum = 1 / (np.sqrt(16.0 * (mAr + mAr) * 300 * 8.62e-5 * c**2
-                        / (3.0 * np.pi * mAr * mAr)) * se * mAr * 1.6e-19 / c**2)
+    nmum = 0.0
+    nmui = 8.0e19
     nDe  = 3.86e22   # argon number density times electron diffusivity [1/(cm*s)]
     nDi  = 2.07e18   # argon number density times ion diffusivity [1/(cm*s)]
     nDm  = 2.42e18   # argon number density times metastable diffusivity [1/(cm*s)]
 
-    # reaction parameters (NB: k_i = Ck*exp(-A/Te))
-    #Ck = np.array([1.235e-7,3.712e-8,2.05e-7,1.818e-9,2e-7]) # pre-exponential factors [cm^3/s]
-    #A  = np.array([18.687,15.06,4.95,2.14,0.0])      # activation temperature [eV]
-    #dH = np.array([15.7,11.56,4.14,-11.56,0.0])        # energy lost per electron due to ionization rxn [eV]
-
-    #Ck = np.array([1.235e-7,3.712e-8,2.05e-7,1.818e-9]) # pre-exponential factors [cm^3/s]
-    #A  = np.array([18.687,15.06,4.95,2.14])      # activation temperature [eV]
-    #dH = np.array([15.7,11.56,4.14,-11.56])        # energy lost per electron due to ionization rxn [eV]
-
-    #Ck = np.array([1.235e-7,0.0,0.0,0.0,0.0]) # pre-exponential factors [cm^3/s]
-    #A  = np.array([18.687,15.06,4.95,2.14,0.0])      # activation temperature [eV]
-    #dH = np.array([15.7,0.0,0.0,0.0,0.0])        # energy lost per electron due to ionization rxn [eV]
-
+    # reaction parameters (NB: k_i = Ck*Ee^B*exp(-A/Ee))
+    #                          Ee = 3/2*Te (Te in eV)
+    #                          -> k_i = [Ck*(2/3)^B] * Ee^B * exp[-(3/2)*A/Ee]
     # nominal
-    # Ck = np.array([1.235e-7,3.712e-8,2.05e-7,1.818e-9,2e-7,6.2e-10,3.0e-15,1.1e-31]) # pre-exponential factors [cm^3/s]
-    Ck = np.array([1.235e-7,3.712e-8,2.05e-7,4.0e-13,2e-7,5.0e-10,2.5e-15]) # pre-exponential factors [cm^3/s]
-
-    # slow excitation rate
-    #Ck = np.array([1.235e-7,0.5*3.712e-8,2.05e-7,1.818e-9,2e-7,6.2e-10,3.0e-15,1.1e-31]) # pre-exponential factors [cm^3/s]
-
-    ## fast excitation rate
-    #Ck = np.array([1.235e-7,2.0*3.712e-8,2.05e-7,1.818e-9,2e-7,6.2e-10,3.0e-15,1.1e-31]) # pre-exponential factors [cm^3/s]
-
-    # A  = np.array([18.687,15.06,4.95,2.14,0.0,0.0,0.0,0.0]) # activation temperature [eV]
-    # dH = np.array([15.7,11.56,4.14,-11.56,0.0,0.0,0.0,0.0]) # energy lost per electron due to ionization rxn [eV]
-    # dEps = np.array([0.0,15.7,11.56,0.0])
-
-    A  = np.array([18.687,15.06,4.95,0.0,0.0,0.0,0.0]) # activation temperature [eV]
-    dH = np.array([15.76,11.56,4.2,0.0,-11.56,-7.56,-11.56]) # energy lost per electron due to ionization rxn [eV]
-    dEps = np.array([0.0,15.76,11.56,0.0])
+    Ck = np.array([2.0e-7,2.1e-9,5.0e-10,6.4e-10,2.1e-15,1.32e8,0.0,3.0e7,3.0e7,0.0,0.0,0.0,0.0,4.3e-10,0.0,3.7e-8,8.9e-7,1.8e-7,3.0e-7,3.0e-7,4.3e-10,9.1e-7,8.9e-7,5e-12,0.0,4e-13,0.0,0.0,5.0e-27,7.17e-27,2.5e-11,2.5e-11,1.0e-9,1.0e-9]) # pre-exponential factors [cm^3/s]
+    B  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0.74,0,0,0.0,0.61,0.0,0.0,0.74,0,0.0,0.0,0.0,-0.5,0.0,0.0,-4.5,-4.5,0.0,0.0,0.0,0.0])
+    A  = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,2.61,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # activation temperature [eV]
+    dH = np.array([0.0,-7.412,-10.054,-7.336,0.0,0.0,0.0,0.0,0.0,11.548,11.624,12.907,15.76,-11.548,4.212,0.076,1.359,2.853,-1.283,-1.359,-11.624,-0.076,1.283,0.0,0.0,0.0,-4.212,-4.136,-2.853,-15.76,-1.359,-1.283,-8.695,-8.771]) # energy lost per electron due to ionization rxn [eV]
+    dEps = np.array([0.0,15.76,11.548,11.624,12.907,0.0])
 
     # BC parameters
     # ks = 1.19e7  # electron recombination rate [cm/s]
@@ -139,8 +119,11 @@ def setPsaapPropertiesTestArm(gam, inputV0, inputVDC, params, Nr, iSample):
     nDm  *= 100.
     nmue *= 100. # 1/(V*m*s)
     nmui *= 100. # 1/(V*m*s)
-    Ck   *= 1e-6 # m^3/s
-    # Ck[7] *= 1e-6 # Ck[7] is now in m^6/s
+    Ck[0:5] *= 1e-6 # m^3/s
+    Ck[5:9] *= 1 # 1/s
+    Ck[9:26]  *= 1e-6 # m^3/s
+    Ck[26:30] *= 1e-12 # m^6/s
+    Ck[30:] *= 1e-6 # m^3/s
     ks   *= 0.01 # m/s
     se   *= 1.0e-20  # m^2
 
@@ -162,11 +145,14 @@ def setPsaapPropertiesTestArm(gam, inputV0, inputVDC, params, Nr, iSample):
     mui   = mui*V0*tau/(L*L)
     mum   = mum*V0*tau/(L*L)
 
-    Ck[0:2] = Ck[0:2]*tau*nAr
-    #Ck[2:5] = Ck[2:5]*tau*np0
-    Ck[2:6] = Ck[2:6]*tau*np0
-    Ck[6]  *= tau*nAr
-    # Ck[7]  *= tau*nAr*nAr
+    Ck[0:4]  *= tau*np0
+    Ck[4]    *= tau*nAr
+    Ck[5:9]  *= tau
+    Ck[9:13] *= tau*nAr
+    Ck[13:26]  *= tau*np0
+    Ck[26:30] *= tau*np0*np0
+    Ck[30:32] *= tau*nAr
+    Ck[32:] *= tau*np0
     A       = A*1.5/e0  # 1.5 to convert from temperature to energy
     dH      = dH/e0
     qStar   = V0/e0 # qe*V0/e0, since e0 in eV, need qe*V0 in eV, which is just V0 in V
@@ -177,14 +163,91 @@ def setPsaapPropertiesTestArm(gam, inputV0, inputVDC, params, Nr, iSample):
                                 # (2/3)*tau/L**2*Kb/np0/kB,
                                 # where Kb is the thermal conductivity of background specie
 
-    #params.beta = np.array([[2,2,2,1,1],[1,0,1,0,0],[0,1,0,0,0],[0,0,0,1,1]])
-    #params.alfa = np.array([[1,1,1,1,1],[0,0,0,0,0],[0,0,1,1,1],[1,1,0,0,0]])
-    #params.beta = np.array([[2,1,2,1],[1,0,1,0],[0,1,0,0],[0,0,0,1]], dtype=np.int)
-    #params.alfa = np.array([[1,1,1,1],[0,0,0,0],[0,0,1,1],[1,1,0,0]], dtype=np.int)
-    # params.beta = np.array([[2,1,2,1,1,1,0,0],[1,0,1,0,0,1,0,0],[0,1,0,0,0,0,0,0],[0,0,0,1,0,1,2,1]], dtype=np.int64)
-    # params.alfa = np.array([[1,1,1,1,1,0,0,0],[0,0,0,0,0,0,0,0],[0,0,1,1,1,2,1,1],[1,1,0,0,0,0,1,2]], dtype=np.int64)
-    params.beta = np.array([[2,1,2,0,1,1,0],[1,0,1,0,0,1,0],[0,1,0,1,0,0,0],[0,0,0,0,1,1,2]], dtype=np.int64)
-    params.alfa = np.array([[1,1,1,1,1,0,0],[0,0,0,1,0,0,0],[0,0,1,0,1,2,1],[1,1,0,0,0,0,1]], dtype=np.int64)
+    params.beta = np.array([[0,1,1,1,0,0,0,0,0,1,1,1,2,1,2,1,1,2,1,1,1,1,1,0,0,0,1,1,1,1,0,0,1,1],                     # E
+                            [0,1,1,1,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1],                     # AR+
+                            [0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,1,0,0,0,1,0,0,0],                     # AR(m)
+                            [0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0],                     # AR(r)
+                            [0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0],                     # AR(4p)
+                            [2,1,1,1,2,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,1]], dtype=np.int64)    # AR
+
+    params.alfa = np.array([[0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,0,0,0,0],                     # E
+                            [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,0,0,0,0],                     # AR+
+                            [2,1,0,2,1,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0],                     # AR(m)
+                            [0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,1],                     # AR(r)
+                            [0,0,2,0,0,0,1,1,1,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,1,1,1,1],                     # AR(4p)
+                            [0,0,0,0,1,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0]], dtype=np.int64)    # AR
+    # Rxn1:  2AR(m)         ->   2AR
+	# Rxn2:  AR(m) + AR(r)  ->   E + AR+ + AR
+	# Rxn3:  2AR(4p)        ->   E + AR+ + AR
+	# Rxn4:  2AR(m)         ->   E + AR+ + AR
+	# Rxn5:  AR(m) + AR     ->   2AR
+	# Rxn6:  AR(r)          ->   AR + hv
+	# Rxn7:  AR(4p)         ->   AR + hv
+	# Rxn8:  AR(4p)         ->   AR(m) + hv
+	# Rxn9:  AR(4p)         ->   AR(r) + hv
+    # Rxn10: E + AR         ->   E + AR(m)
+    # Rxn11: E + AR         ->   E + AR(r)
+    # Rxn12: E + AR         ->   E + AR(4p)
+    # Rxn13: E + AR         ->   2E + AR+
+    # Rxn14: E + AR(m)      ->   E + AR
+    # Rxn15: E + AR(m)      ->   2E + AR+
+    # Rxn16: E + AR(m)      ->   E + AR(r)
+    # Rxn17: E + AR(m)      ->   E + AR(4p)
+    # Rxn18: E + AR(4p)     ->   2E + AR+
+    # Rxn19: E + AR(4p)     ->   E + AR(r)
+    # Rxn20: E + AR(4p)     ->   E + AR(m)
+    # Rxn21: E + AR(r)      ->   E + AR
+    # Rxn22: E + AR(r)      ->   E + AR(m)
+    # Rxn23: E + AR(r)      ->   E + AR(4p)
+    # Rxn24: E + AR+        ->   AR(m) + hv
+    # Rxn25: E + AR+        ->   AR(r) + hv
+    # Rxn26: E + AR+        ->   AR(4p) + hv
+    # Rxn27: 2E + AR+       ->   E + AR(m)
+    # Rxn28: 2E + AR+       ->   E + AR(r)
+    # Rxn29: 2E + AR+       ->   E + AR(4p)
+    # Rxn30: 2E + AR+       ->   E + AR
+    # Rxn31: AR + AR(4p)    ->   AR + AR(m)
+    # Rxn32: AR + AR(4p)    ->   AR + AR(r)
+    # Rxn33: AR(m) + AR(4p) ->   E + AR+ + AR
+    # Rxn34: AR(r) + AR(4p) ->   E + AR+ + AR
+    # Rxn35: E + AR(r)      ->   2E + AR+ (not considered here)
+
+    rxnNameDict = {0: "Ar(m) + Ar(m) => Ar + Ar",
+                   1: "Ar(m) + Ar(r) => E + Ar + Ar+",
+                   2: "Ar(4p) + Ar(4p) => E + Ar + Ar+",
+                   3: "Ar(m) + Ar(m) => E + Ar + Ar+",
+                   4: "Ar + Ar(m) => Ar + Ar",
+                   5: "Ar(r) => Ar",
+                   6: "Ar(4p) => Ar",
+                   7: "Ar(4p) => Ar(m)",
+                   8: "Ar(4p) => Ar(r)",
+                   9: "1s-metastable",
+                   10: "1s-resonance",
+                   11: "2p-lumped",
+                   12: "Ionization",
+                   13: "E + Ar(m) => E + Ar",
+                   #13: "deexci-metastable",
+                   14: "StepIonization",
+                   15: "E + Ar(m) => E + Ar(r)",
+                   16: "E + Ar(m) => E + Ar(4p)",
+                   17: "E + Ar(4p) => E + E + Ar+",
+                   18: "E + Ar(4p) => E + Ar(r)",
+                   19: "E + Ar(4p) => E + Ar(m)",
+                   20: "E + Ar(r) => E + Ar",
+                   #20: "deexci-resonance",
+                   21: "E + Ar(r) => E + Ar(m)",
+                   22: "E + Ar(r) => E + Ar(4p)",
+                   23: "E + Ar+ => Ar(m)",
+                   24: "E + Ar+ => Ar(r)",
+                   25: "E + Ar+ => Ar(4p)",
+                   26: "2E + Ar+ => E + Ar(m)",
+                   27: "2E + Ar+ => E + Ar(r)",
+                   28: "2E + Ar+ => E + Ar(4p)",
+                   29: "2E + Ar+ => E + Ar",
+                   30: "Ar + Ar(4p) => Ar + Ar(m)",
+                   31: "Ar + Ar(4p) => Ar + Ar(r)",
+                   32: "Ar(m) + Ar(4p) => E + Ar+ + Ar",
+                   33: "Ar(r) + Ar(4p) => E + Ar+ + Ar"}
 
     # 4) Set values in params class
     params.D[0]    = De
@@ -196,9 +259,13 @@ def setPsaapPropertiesTestArm(gam, inputV0, inputVDC, params, Nr, iSample):
     params.mu[2]   = mum
 
     params.A[:]    = Ck[:]
-    params.B[:]    = 0.0
+    params.B[:]    = B[:]
     params.C[:]    = A[:]
 
+    # Account for the 2/3 term to convert from electron temperature to electron energy
+    for i in range(len(params.A)):
+        params.A[i] *= (2/3)**(params.B[i])
+
     params.dH[:]   = dH[:]
     params.dEps[:] = dEps[:]
     params.qStar   = qStar
@@ -226,61 +293,33 @@ def setPsaapPropertiesTestArm(gam, inputV0, inputVDC, params, Nr, iSample):
     params.eps0    = eps0          # unit charge [C]
     params.eArea   = electrodeArea # electrode area [m^2]
 
-    # reactionExpressionslist = [f"{params.A[0]} * energy**{params.B[0]} * np.exp(-{params.C[0]} / energy)",
-    #                            f"{params.A[1]} * energy**{params.B[1]} * np.exp(-{params.C[1]} / energy)",
-    #                            f"{params.A[2]} * energy**{params.B[2]} * np.exp(-{params.C[2]} / energy)",
-    #                            f"{params.A[3]} * energy**{params.B[3]} * np.exp(-{params.C[3]} / energy)",
-    #                            f"{params.A[4]} * energy**{params.B[4]} * np.exp(-{params.C[4]} / energy)",
-    #                            f"{params.A[5]} * energy**{params.B[5]} * np.exp(-{params.C[5]} / energy)",
-    #                            f"{params.A[6]} * energy**{params.B[6]} * np.exp(-{params.C[6]} / energy)",
-    #                            f"{params.A[7]} * energy**{params.B[7]} * np.exp(-{params.C[7]} / energy)"]
-
-    # reactionTExpressionslist = [f"{params.A[0]} * (energy**({params.B[0]}-1)) * np.exp(-{params.C[0]}/energy) * ({params.B[0]} + {params.C[0]}/energy)",
-    #                             f"{params.A[1]} * (energy**({params.B[1]}-1)) * np.exp(-{params.C[1]}/energy) * ({params.B[1]} + {params.C[1]}/energy)",
-    #                             f"{params.A[2]} * (energy**({params.B[2]}-1)) * np.exp(-{params.C[2]}/energy) * ({params.B[2]} + {params.C[2]}/energy)",
-    #                             f"{params.A[3]} * (energy**({params.B[3]}-1)) * np.exp(-{params.C[3]}/energy) * ({params.B[3]} + {params.C[3]}/energy)",
-    #                             f"{params.A[4]} * (energy**({params.B[4]}-1)) * np.exp(-{params.C[4]}/energy) * ({params.B[4]} + {params.C[4]}/energy)",
-    #                             f"{params.A[5]} * (energy**({params.B[5]}-1)) * np.exp(-{params.C[5]}/energy) * ({params.B[5]} + {params.C[5]}/energy)",
-    #                             f"{params.A[6]} * (energy**({params.B[6]}-1)) * np.exp(-{params.C[6]}/energy) * ({params.B[6]} + {params.C[6]}/energy)",
-    #                             f"{params.A[7]} * (energy**({params.B[7]}-1)) * np.exp(-{params.C[7]}/energy) * ({params.B[7]} + {params.C[7]}/energy)"]
-
-    reactionExpressionslist = [f"{params.A[0]} * energy**{params.B[0]} * np.exp(-{params.C[0]} / energy)",
-                                f"{params.A[1]} * energy**{params.B[1]} * np.exp(-{params.C[1]} / energy)",
-                                f"{params.A[2]} * energy**{params.B[2]} * np.exp(-{params.C[2]} / energy)",
-                                f"{params.A[3]} * energy**({params.B[3]}-0.5)",
-                                f"{params.A[4]} * energy**{params.B[4]} * np.exp(-{params.C[4]} / energy)",
-                                f"{params.A[5]} * energy**{params.B[5]} * np.exp(-{params.C[5]} / energy)",
-                                f"{params.A[6]} * energy**{params.B[6]} * np.exp(-{params.C[6]} / energy)"]
-
-    reactionTExpressionslist = [f"{params.A[0]} * (energy**({params.B[0]}-1)) * np.exp(-{params.C[0]}/energy) * ({params.B[0]} + {params.C[0]}/energy)",
-                                f"{params.A[1]} * (energy**({params.B[1]}-1)) * np.exp(-{params.C[1]}/energy) * ({params.B[1]} + {params.C[1]}/energy)",
-                                f"{params.A[2]} * (energy**({params.B[2]}-1)) * np.exp(-{params.C[2]}/energy) * ({params.B[2]} + {params.C[2]}/energy)",
-                                f"{params.A[3]} * (energy**({params.B[3]}-0.5-1)) * np.exp(-{params.C[3]}/energy) * ({params.B[3]}-0.5 + {params.C[3]}/energy)",
-                                f"{params.A[4]} * (energy**({params.B[4]}-1)) * np.exp(-{params.C[4]}/energy) * ({params.B[4]} + {params.C[4]}/energy)",
-                                f"{params.A[5]} * (energy**({params.B[5]}-1)) * np.exp(-{params.C[5]}/energy) * ({params.B[5]} + {params.C[5]}/energy)",
-                                f"{params.A[6]} * (energy**({params.B[6]}-1)) * np.exp(-{params.C[6]}/energy) * ({params.B[6]} + {params.C[6]}/energy)"]
-
-    reactionExpressionTypelist =  np.array([True,True,True,False,False,False,False])
+
+    reactionExpressionTypelist =  np.array([False,False,False,False,False,False,False,False,False,
+                                           True,True,True,True,False,True,True,True,False,True,True,False,True,True,
+                                           False,False,False,False,False,False,False,False,False,False,False])
 
     reactionsList = []
     LOGFilename = 'interpolationSample%s.log'%str(iSample)
     f = open(LOGFilename, 'w')
+
     for i in range(Nr):
+        sample_root_dir = "../BOLSIGChemistry_6SpeciesRates_New"
         if reactionExpressionTypelist[i]:
-            Nsample = 72
-            N300 = 200
-
-            root_dir = "/g/g92/jbarbere/glowDischarge/toyProblems/timeDomain"
-            rate_file = "{0:s}/BOLSIGChemistry/reaction300K_{1:d}.dat".format(root_dir, i)
-            temp_file = "{0:s}/BOLSIGChemistry/reaction300K.Te.dat".format(root_dir)
-
-            #rateCoeff = np.fromfile('/usr/workspace/violetak/BOLSIGSamples/glowDischarge/toyProblems/timeDomain/BOLSIGChemistry/reaction300K_%s.dat' %str(i))
-            rateCoeff = np.fromfile(rate_file)
-            rateCoeff = np.reshape(rateCoeff,[Nsample, N300]).T[:,iSample]
-
-            #Te = np.fromfile('/usr/workspace/violetak/BOLSIGSamples/glowDischarge/toyProblems/timeDomain/BOLSIGChemistry/reaction300K.Te.dat')
-            Te = np.fromfile(temp_file)
-            Te = np.reshape(Te,[Nsample, N300]).T[:,iSample]
+            if i < 15 or i == 20:
+                fileString = sample_root_dir + "/" + rxnNameDict[i]
+                fileName = "%s.%08d.h5" % (fileString, iSample)
+                f = h5.File(fileName, 'r')
+                dataset = f["table"]
+            else:
+                fileString = sample_root_dir + "/" + "StepwiseExcitations"
+                fileName = "%s.%08d.h5" % (fileString, iSample)
+                f = h5.File(fileName, 'r')
+                dataset = f[rxnNameDict[i]]
+            
+            rateCoeff = dataset[:,1]
+            rateCoeff /= 6.022e23
+            Te = dataset[:,0]
+            Te /= 11604.
 
             # Sorting mean energy array and rate coefficient array based on
             # the mean energy array.
@@ -295,7 +334,7 @@ def setPsaapPropertiesTestArm(gam, inputV0, inputVDC, params, Nr, iSample):
             Te = Te[TeDuplicateinds]
 
             # Nondimensionalization of mean energy.
-            # Te *= 1.5
+            Te *= 1.5
 
             # Find first non-zero value of the coefficient rate.
             I = np.nonzero(rateCoeff)
@@ -314,7 +353,10 @@ def setPsaapPropertiesTestArm(gam, inputV0, inputVDC, params, Nr, iSample):
 
             indices = Nan + Inf + Positive
 
-            lastFalse = np.where(indices==False)[-1][-1] + 2
+            #lastFalse = np.where(indices==False)[-1][-1] + 2
+            for k in range(len(Te)):
+                if (Te[k] < 4.5 and indices[k] == False):
+                    lastFalse = k + 2
 
             # Transformation to log scale.
             TeLog = np.log(Te)
@@ -336,7 +378,13 @@ def setPsaapPropertiesTestArm(gam, inputV0, inputVDC, params, Nr, iSample):
             # For the troublesome values, we use the Arrhenius form.
             rateCoeffLog[0:lastFalse] = ALog - C / Te[0:lastFalse]
             # Nondimensionalization in log scale.
-            if i < 2:
+            if i == 9:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
+            elif i == 10:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
+            elif i == 11:
+                rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
+            elif i == 12:
                 rateCoeffLog += - np.log(1.0/tau) + np.log(nAr)
             else:
                 rateCoeffLog += - np.log(1.0/tau) + np.log(np0)
@@ -359,37 +407,36 @@ def setPsaapPropertiesTestArm(gam, inputV0, inputVDC, params, Nr, iSample):
             logging.warning('Position of possible duplicates in mean energy array: %s',
                           TeDuplicateindsForLog[0])
 
-            # rxn   = eval("lambda energy :" + reactionExpressionslist[i])
-            # rxn_T = eval("lambda energy :" + reactionTExpressionslist[i])
-            # # setting the axes at the centre
-            # fig ,ax = plt.subplots(figsize=(9, 6))
-            # ax.spines["top"].set_visible(True)
-            # ax.spines["right"].set_visible(True)
-            # ax.set_yscale('log')
-            # ax.set_xscale('log')
-
-            # # plot the function
-            # # plt.plot(rateCoeffXFiner, np.exp(reactionExpressions_cubicSplineDerivative_log(rateCoeffXFiner)),
-            # #  		 color='salmon', linestyle='--', label='interBolsig')
-            # # plt.plot(rateCoeffXFine, np.exp(reactionExpressions_cubicSpline_log(rateCoeffXFine)),
-            # #  		 color='lightgreen', linestyle='--', label='interBolsig')
-            # # plt.plot(Te[:,0], reactionTExpressionsLogFiltered(TeLog[:]) * np.exp(reactionExpressionsLog(TeLog[:])) / Te[:,0],
-            # #  		 color='blue', linestyle='-', label='interBolsig')
-            # plt.plot(Te, np.exp(reactionExpressionsLog(TeLog[:])),
-            #  		 color='green', linestyle='-', label='Bolsig')
-            # plt.plot(Te, rxn(Te),
-            #  		 color='salmon', linestyle='--', label='Liu')
-            # # plt.plot(Te[:,0], rxn_T(Te[:,0]),
-            # #  		 color='red', linestyle='--', label='interBolsig')
-            # plt.xlim((0.05,100))
-            # plt.ylim((1e-50,300))
-            # plt.legend()
-            # plt.savefig("./Rates_%s.pdf" %str(i), dpi=300)
-            # # plt.xlim((-0.0001,0.0255))
-            # plt.show()
         else:
-            rxn   = eval("lambda energy :" + reactionExpressionslist[i])
-            rxn_T = eval("lambda energy :" + reactionTExpressionslist[i])
+            fileName = "%s/Arrhenius.%08d.h5" % (sample_root_dir, iSample)
+            f = h5.File(fileName, 'r')
+            arrh_Coeffs = f[rxnNameDict[i]][...]
+            A, B, C = arrh_Coeffs
+
+            # Non-dimensionalize the coefficients
+            if i < 4:
+                A /= 6.022e23
+                A *= tau*np0
+            elif i == 4:
+                A /= 6.022e23
+                A *= tau*nAr
+            elif i > 4 and i < 9:
+                A *= tau
+            elif i > 25 and i < 30:
+                A /= (6.022e23)**2
+                A *= tau*np0*np0
+            elif i > 29 and i < 32:
+                A /= 6.022e23
+                A *= tau*nAr
+            else:
+                A /= 6.022e23
+                A *= tau*np0
+
+            A *= ((2./3.)*11604)**B 
+            C *= 1.5/(e0*11604)
+
+            rxn = eval("lambda energy :" + f"{A} * energy**{B} * np.exp(-{C} / energy)")
+            rxn_T = eval("lambda energy :" + f"{A} * energy**({B}-1) * np.exp(-{C} / energy) * ({B} + {C} / energy)")
 
             reaction = Reaction(rxnAlfa = params.alfa[:,[i]], rxnBeta = params.beta[:,[i]],
                                 rxnBolsig = reactionExpressionTypelist[i],
@@ -397,31 +444,42 @@ def setPsaapPropertiesTestArm(gam, inputV0, inputVDC, params, Nr, iSample):
             reactionsList.append(reaction)
 
     params.reactionsList = reactionsList
-    #params.Nr = 1
 
     diffList = []
-    Te = np.linspace(0, 1000, 10)
-    De_interp = params.D[0]*np.ones(10)
-    De_spline = CubicSpline(Te, De_interp)
+    # Data from BOLSIG
+    # Te in [eV]
+    # De * N in [1/(m*s)]
+    transport = h5.File("../BOLSIGChemistry_6SpeciesRates_New/Transport.%08d.h5" % (iSample), 'r')
+    #Te = NDe_v_Te[:,0]
+    NDe_v_Te = transport["diffusivity"]
+    Te_trans = NDe_v_Te[:,0]
+    Te_trans /= 11604
+    print("Te_min = {0:.6e}".format(NDe_v_Te[0,0]))
+    print("Te_max = {0:.6e}".format(NDe_v_Te[-1,0]))
+    De_interp = (NDe_v_Te[:,1]/nAr)*tau/(L*L)
+    De_spline = CubicSpline(Te_trans, De_interp)
     De_Te_spline = CubicSpline.derivative(De_spline)
-    diffusivity = Diffusivity(interpolate = False, D_expression = De_spline, D_T_expression = De_Te_spline)
+    diffusivity = Diffusivity(interpolate = True, D_expression = De_spline, D_T_expression = De_Te_spline)
     diffList.append(diffusivity)
 
-    Ns = 4
+    Ns = 6
     for i in range(1, Ns):
         diffList.append(Diffusivity(interpolate = False))
 
     params.diffusivityList = diffList
 
     muList = []
-    Te = np.linspace(0, 1000, 10)
-    mue_interp = params.mu[0]*np.ones(10)
-    mue_spline = CubicSpline(Te, mue_interp)
+    # Data from BOLSIG
+    # Te in [eV]
+    # Mue * N in [1/(V*m*s)]
+    Nmue_v_Te = transport["mobility"]
+    mue_interp = (Nmue_v_Te[:,1]/nAr)*V0*tau/(L*L)
+    mue_spline = CubicSpline(Te_trans, mue_interp)
     mue_Te_spline = CubicSpline.derivative(mue_spline)
-    mobility = Mobility(interpolate = False, mu_expression = mue_spline, mu_T_expression = mue_Te_spline)
+    mobility = Mobility(interpolate = True, mu_expression = mue_spline, mu_T_expression = mue_Te_spline)
     muList.append(mobility)
 
-    Ns = 4
+    Ns = 6
     for i in range(1, Ns):
         muList.append(Mobility(interpolate = False))
 
diff --git a/sampleRun.sh b/sampleRun.sh
deleted file mode 100755
index af1297760..000000000
--- a/sampleRun.sh
+++ /dev/null
@@ -1,66 +0,0 @@
-#!/bin/bash
-
-error_exit()
-{
-  echo "$1" 1>&2
-  exit 1
-}
-
-module load python/3.8.2
-EXE="python3 ../chebSolver.py"
-NEWTEXE="python3 ../timePeriodicSolver.py"
-
-# psaap 2 species case
-#Np=250
-#Nt=2560
-#dt=0.00390625
-#baseFile='restart_psaap_Np250_'
-
-# Liu 3 species case
-#Np=250
-#Nt=25600
-#Nt1=128
-#dt=0.0078125
-#scenario=0
-#baseFile="restart_3spec_Np${Np}_"
-#newtFile="newton_3spec_Np${Np}.npy"
-#saveFile="newton_3spec_Np${Np}_fullsoln.npy"
-
-# Liu 4 species case
-Np=150
-Nt=25600
-Nt1=128
-dt=0.0078125
-scenario=6
-baseFile="restart_4spec_Np${Np}_"
-newtFile="newton_4spec_Np${Np}.npy"
-saveFile="newton_4spec_Np${Np}_fullsoln.npy"
-
-baseCmd="$EXE --Np $Np --Nt $Nt --dt $dt --scenario $scenario"
-newtCmd="$NEWTEXE --Np $Np --Nt $Nt1 --Nn 10 --scenario $scenario"
-saveCmd="$EXE --Np $Np --Nt $Nt1 --dt $dt --scenario $scenario"
-
-# Activation of background specie and elastic collisions.
-#baseCmd="$EXE --Np $Np --Nt $Nt --dt $dt --scenario $scenario --elasticCollisionActivation --backgroundSpecieActivation"
-#newtCmd="$NEWTEXE --Np $Np --Nt $Nt1 --Nn 10 --scenario $scenario --elasticCollisionActivation --backgroundSpecieActivation"
-#saveCmd="$EXE --Np $Np --Nt $Nt1 --dt $dt --scenario $scenario --elasticCollisionActivation --backgroundSpecieActivation"
-
-screenOut="run.out"
-rm -f $screenOut
-
-# Run an example case of the Chebyshev time domain solver
-echo "Run 0 to 200...${baseFile}T200.npy"
-$baseCmd --V0 100 --VDC 0.0 --t0   0.0 --outfile "${baseFile}T200.npy"  > $screenOut || error_exit "First run failed"
-echo "Run 200 to 400...${baseFile}T400.npy"
-$baseCmd --V0 100 --VDC 0.0 --t0 200.0 --restart "${baseFile}T200.npy"  \
-                --outfile "${baseFile}T400.npy" >> $screenOut || error_exit "Second run failed"
-echo "Run 400 to 600...${baseFile}T600.npy"
-$baseCmd --V0 100 --VDC 0.0 --t0 400.0 --restart "${baseFile}T400.npy"  \
-                --outfile "${baseFile}T600.npy" >> $screenOut || error_exit "Second run failed"
-
-echo "Run time domain shooting...${newtFile}"
-$newtCmd --V0 100 --VDC 0.0 --gam 0.01 --rtol 1e-8 --restart "${baseFile}T600.npy" \
-                                --outfile $newtFile >> $screenOut || error_exit "Shooting failed"
-
-echo "Saving one period...${saveFile}"
-$saveCmd --V0 100 --VDC 0.0 --rtol 1e-8 --restart $newtFile --savedata $saveFile --outfile discard.npy >> $screenOut
diff --git a/sampleRunCN.sh b/sampleRunCN.sh
deleted file mode 100755
index fb6a9f314..000000000
--- a/sampleRunCN.sh
+++ /dev/null
@@ -1,70 +0,0 @@
-#!/bin/bash
-
-error_exit()
-{
-  echo "$1" 1>&2
-  exit 1
-}
-
-module load python/3.8.2
-EXE="python3 ./chebSolver.py"
-NEWTEXE="python3 ./timePeriodicSolver.py"
-
-# psaap 2 species case
-#Np=250
-#Nt=2560
-#dt=0.00390625
-#baseFile='restart_psaap_Np250_'
-
-# Liu 3 species case
-#Np=300
-#Nt=25600
-#Nt1=128
-#dt=0.0078125
-#scenario=0
-#baseFile="restart_3spec_CN_Np${Np}_"
-#newtFile="newton_3spec_CN_Np${Np}.npy"
-#saveFile="newton_3spec_CN_Np${Np}_fullsoln.npy"
-
-# Liu 4 species case
-Np=150
-Nt=25600
-Nt1=128
-dt=0.0078125
-scenario=6
-baseFile="restart_4spec_CN_Np${Np}_"
-newtFile="newton_4spec_CN_Np${Np}.npy"
-saveFile="newton_4spec_CN_Np${Np}_fullsoln.npy"
-
-baseCmd="$EXE --Np $Np --Nt $Nt --dt $dt --scenario $scenario"
-#newtCmd="$NEWTEXE --Np $Np --Nt $Nt1 --Nn 10 --scenario $scenario --tscheme CN"
-newtCmd="$NEWTEXE --Np $Np --Nt $Nt1 --Nn 20 --scenario $scenario --tscheme CN --alpha0 0.1 --increaseFac 1.5"
-saveCmd="$EXE --Np $Np --Nt $Nt1 --dt $dt --scenario $scenario --tscheme CN"
-
-# Activation of background specie and elastic collisions.
-#baseCmd="$EXE --Np $Np --Nt $Nt --dt $dt --scenario $scenario --elasticCollisionActivation --backgroundSpecieActivation"
-#newtCmd="$NEWTEXE --Np $Np --Nt $Nt1 --Nn 10 --scenario $scenario --tscheme CN --elasticCollisionActivation --backgroundSpecieActivation"
-#saveCmd="$EXE --Np $Np --Nt $Nt1 --dt $dt --scenario $scenario --tscheme CN --elasticCollisionActivation --backgroundSpecieActivation"
-
-screenOut="runCN.out"
-rm -f $screenOut
-
-# Run an example case of the Chebyshev time domain solver
-echo "Run 0 to 200...${baseFile}T200.npy"
-$baseCmd --V0 100 --VDC 0.0 --t0   0.0 --outfile "${baseFile}T200.npy"  > $screenOut || error_exit "First run failed"
-echo "Run 200 to 400...${baseFile}T400.npy"
-$baseCmd --V0 100 --VDC 0.0 --t0 200.0 --restart "${baseFile}T200.npy"  \
-                --outfile "${baseFile}T400.npy" >> $screenOut || error_exit "Second run failed"
-echo "Run 400 to 600...${baseFile}T600.npy"
-$baseCmd --V0 100 --VDC 0.0 --t0 400.0 --restart "${baseFile}T400.npy"  \
-                --outfile "${baseFile}T600.npy" >> $screenOut || error_exit "Second run failed"
-echo "Run 600 to 800...${baseFile}T800.npy"
-$baseCmd --V0 100 --VDC 0.0 --t0 600.0 --restart "${baseFile}T600.npy"  \
-                --outfile "${baseFile}T800.npy" >> $screenOut || error_exit "Second run failed"
-
-echo "Run time domain shooting...${newtFile}"
-$newtCmd --V0 100 --VDC 0.0 --gam 0.01 --rtol 1e-8 --restart "${baseFile}T800.npy" \
-                                --outfile $newtFile >> $screenOut || error_exit "Shooting failed"
-
-echo "Saving one period...${saveFile}"
-$saveCmd --V0 100 --VDC 0.0 --rtol 1e-8 --restart $newtFile --savedata $saveFile --outfile discard.npy >> $screenOut
diff --git a/timePeriodicSolver.py b/timePeriodicSolver.py
index cb239002d..46a0454b3 100644
--- a/timePeriodicSolver.py
+++ b/timePeriodicSolver.py
@@ -23,7 +23,7 @@ def __init__(self, Ns, NT, Np, elasticCollisionActivationFactor,
         else:
             # Default initial guess.  This should be overwritten
             # by reading restart if you want this to work.
-            self.tds.U1[0:self.tds.Np] = 1e-4
+            self.tds.U1[0:self.tds.Np] = 1e-3
             self.tds.U1[self.tds.Np:2*self.tds.Np] = 1e-4
             self.tds.U1[2*self.tds.Np:] = 0.75
 
@@ -180,25 +180,58 @@ def solveNewtonStep(self, Uic, Nt):
         print("#   Running scenario = 3 (4 species, 8 rxn, Liu 2017)")
         Ns = 4
     elif(args.scenario==4):
-        print("#   Running scenario = 4 (4 species, 8 rxn, Liu 2017)")
+        print('#   Running scenario = 4 (4 species, 9 rxn, 1Torr, Nominal)')
         Ns = 4
     elif(args.scenario==5):
-        print("#   Running scenario = 5 (4 species, 7 rxn, Bolsing and Lay, Moss et al, 2003)")
+        print('# Running scenario = 5 (4 species, 9 rxn, 1Torr, Sampling)')
         Ns = 4
     elif(args.scenario==6):
-        print("#   Running scenario = 6 (4 species, 9 rxn, Juan's Mechanism)")
-        Ns = 4
+        print('#   Running scenario = 6 (6 species, 23 rxn, 1Torr, 100V, Sampling)')
+        Ns = 6
     elif(args.scenario==7):
-        print("#   Running scenario = 7 (4 species, 9 rxn, Nominal Reaction Rates)")
-        Ns = 4
+        print('#   Running scenario = 7 (6 species, 23 rxn, 250mTorr, 100V, Sampling)')
+        Ns = 6
     elif(args.scenario==8):
-        Ns = 4
+        print("#   Running scenario = 9 (6 species, 23 rxn, 500mTorr, 100V, Sampling)")
+        Ns = 6
     elif(args.scenario==9):
         print("#   Running scenario = 9 (6 species, 23 rxn)")
         Ns = 6
     elif(args.scenario==10):
+        print("#   Running scenario = 10 (6 species, 23 rxn, 100mTorr, Nominal)")
+        Ns = 6
+    elif(args.scenario==12):
+        print('#   Running scenario = 12 (6 species, 23 rxn, 1Torr, 100V, Nominal)')
+        Ns = 6
+    elif(args.scenario==13):
+        print('#   Running scenario = 13 (6 species, 23 rxn, 500mTorr, 100V, Nominal)')
+        Ns = 6
+    elif(args.scenario==14):
+        print('#   Running scenario = 14 (6 species, 34 rxn, 1Torr, Nominal)')
+        Ns = 6
+    elif(args.scenario==7):
+        print('#   Running scenario = 7 (6 species, 34 rxn, 1Torr, Nominal)')
+        Ns = 6
+    elif(args.scenario==8):
+        print('#   Running scenario = 8 (6 species, 34 rxn, 1Torr, Sampling)')
+        Ns = 6
+    elif(args.scenario==9):
+        print('#   Running scenario = 9 (6 species, 34 rxn, 2Torr, Nominal)')
+        Ns = 6
+    elif(args.scenario==10):
+        print('#   Running scenario = 10 (6 species, 34 rxn, 2Torr, Sampling)')
+        Ns = 6
+    elif(args.scenario==11):
+        print('#   Running scenario = 11 (6 species, 34 rxn, 5Torr, Nominal)')
         Ns = 6
     elif(args.scenario==12):
+        print('#   Running scenario = 12 (6 species, 34 rxn, 5Torr, Sampling)')
+        Ns = 6
+    elif(args.scenario==13):
+        print('#   Running scenario = 13 (6 species, 34 rxn, 10Torr, Nominal)')
+        Ns = 6
+    elif(args.scenario==14):
+        print('#   Running scenario = 14 (6 species, 34 rxn, 10Torr, Sampling)')
         Ns = 6
     elif(args.scenario==21):
         print("#   Running scenario = 21 (4 species, 8 rxn, Liu 2017, interpolated transport)")
@@ -207,6 +240,7 @@ def solveNewtonStep(self, Uic, Nt):
         print("ERROR: Scenario = {0:d} not recognized.  Exiting.".format(args.scenario))
         exit(-1)
 
+
     print("#")
 
     elasticCollisionActivationFactor = 1.0
diff --git a/voltage_sample_gen.py b/voltage_sample_gen.py
new file mode 100644
index 000000000..0a6cd91d8
--- /dev/null
+++ b/voltage_sample_gen.py
@@ -0,0 +1,17 @@
+import numpy as np
+import h5py as h5
+import argparse
+
+voltage_uncert = 1./3. #Volts, given as standard deviation, assuming a normal distribution
+#parser = argparse.ArgumentParser(description="")
+#parser.add_argument(
+
+for i in range(7200):
+    factor = np.random.normal(0, voltage_uncert)
+    with h5.File('./BOLSIGChemistry_Voltage/Voltage.%08d.h5' % (i), 'w') as f:
+        f.create_dataset('V_Err', data = [factor])
+
+    if (i % 100) == 0:
+        print('{}-th sample generated'.format(i))
+
+print('Done!')