syncing 50% EtOH changes from master branch

quantity_module/data/solvent-viscosity.xlsx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:967a90804b91a62ab8220affebd22eabdd459803340211ec3548618f5a0aba72
-size 18595
+oid sha256:54dcc8e1abe242707b2039ff8735a7a986b28023cc23ef9a9e96f3aa5223b451
+size 13816

quantity_module/functions.py

@@ -21,6 +21,7 @@ N_sample = int(1e6)
 
 ## list of solvents to include, all semi-polar and non-polar solvents in ISO 10993-18:2020 Table D.1 (except DMSO, which is not used in practice)
 solvents = ['acetonitrile','methanol','acetone','ethanol','tetrahydrofuran','propanol','isopropanol','dichloromethane','toluene','cyclohexane','heptane','hexane']
+#solvents = ['acetonitrile','methanol','acetone','ethanol','50% ethanol','tetrahydrofuran','propanol','isopropanol','dichloromethane','toluene','cyclohexane','heptane','hexane']
 
 ## c distribution parameters
 T_cut = 20
@@ -68,23 +69,42 @@ else:
     df_final = pd.merge(df_final, df_desc[['Solute_InChIKey', 'Vabc', 'VMcGowan']], how='left', on='Solute_InChIKey', suffixes=('', '_dupe'))
 
 #### solvent-specific viscosity
-# add MW
-mws = []
-for solv in df_visc['Solvent_Name']:
-    mw = chemicals.search_chemical(solv).MW
-    mws.append(mw)
-df_visc['MW'] = mws
+# add MW -- already in the spreadsheet
+#mws = []
+#for solv in df_visc['Solvent_Name']:
+#    mw = chemicals.search_chemical(solv).MW
+#    mws.append(mw)
+#df_visc['MW'] = mws
 ## selected solvent MWs
-Solvent_MWs = {solv:df_visc.loc[df_visc['Solvent_Name']==solv,'MW'].iloc[0] for solv in solvents}
+#Solvent_MWs = {solv:df_visc.loc[df_visc['Solvent_Name']==solv,'MW'].iloc[0] for solv in solvents}
+Solvent_MWs = dict(df_visc[['Solvent_Name','MW']].itertuples(index=False))
 #Solvent_Densities = {solv:string2density(solv)[0] for solv in solvents} # can fail sometimes, so these are hardcoded for now
 #Solvent_Densities = {'ethanol': 0.79, 'isopropanol': 0.787515, 'acetonitrile': 0.78467, 'toluene': 0.86925, 'dichloromethane': 1.326875, 'hexane': 0.6602}
-Solvent_Densities = {'acetonitrile': 0.7847, 'methanol': 0.7955, 'acetone': 0.7893, 'ethanol': 0.7898, 'tetrahydrofuran': 0.8878, 'propanol': 0.8057, 'isopropanol': 0.787, 'dichloromethane': 1.3269, 'toluene': 0.8673, 'cyclohexane': 0.7793, 'heptane': 0.6808, 'hexane': 0.6599}
+Solvent_Densities = {'acetonitrile': 0.7847, 'methanol': 0.7955, 'acetone': 0.7893, 'ethanol': 0.7898, '50% ethanol': (0.7898+1)/2, 'tetrahydrofuran': 0.8878, 'propanol': 0.8057, 'isopropanol': 0.787, 'dichloromethane': 1.3269, 'toluene': 0.8673, 'cyclohexane': 0.7793, 'heptane': 0.6808, 'hexane': 0.6599}
 # linear relation to estimate Vabc when it fails for a molecule
 Vabc = df_desc['Vabc']
 Vmcg = df_desc['VMcGowan']
 m = ~pd.isna(Vabc)
 popt_V = np.polyfit(Vmcg[m], Vabc[m], 1)
 
+# ---- model: Grunberg–Nissan style with polynomial interaction that vanishes at x=0,1 ----
+# fitted to data from R. Belda, J. V. Herráez, O. Diez, Rheological study and thermodynamic analysis of the binary system (water/ethanol): Influence of concentration. Physics and Chemistry of Liquids 42, 467-479 (2004).
+popt_etoh = np.array([-6.35036532e+00, 1.86507282e+03, -5.30902320e+00, 1.60463200e+03, -1.03040657e+01, 3.05646061e+00, -4.93824317e+00, 4.16274239e+03, -1.18411097e+03, 1.69557649e+03])
+def predict_lneta(p, T, x, n_poly=3, interaction_has_T=True):
+    Aw, Bw, Ae, Be = p[:4]  # ln(eta_w)=Aw+Bw/T, ln(eta_e)=Ae+Be/T
+    ln_eta_w = Aw + Bw / T
+    ln_eta_e = Ae + Be / T
+    xc = 2.0*x - 1.0  # map wt frac [0,1] -> [-1,1]
+    Phi = np.vstack([xc**k for k in range(n_poly)])  # (n_poly, N)
+    if interaction_has_T:
+        a = p[4:4+n_poly]
+        b = p[4+n_poly:4+2*n_poly]
+        G = (a @ Phi) + (b @ Phi) / T
+    else:
+        a = p[4:4+n_poly]
+        G = (a @ Phi)
+    return x*ln_eta_e + (1-x)*ln_eta_w + x*(1-x)*G
+
 def get_WC(T,solv,V):
     params = df_visc[df_visc['Solvent_Name']==solv].iloc[0]
     if params['Equation'] == '10^A(1/T-1/B)':
@@ -103,9 +123,12 @@ def get_WC(T,solv,V):
         eta = params['A'] * 298.15/T
     elif params['Equation'] == 'A*T+B':
         eta = params['A'] * T + params['A']
+    elif params['Equation'] == 'fitted_EtOH':
+        # assuming 50% is by volume --> by mass for consistency with fitted model
+        eta = np.exp(predict_lneta(popt_etoh, T, 0.5*0.7898/(0.5*0.7898+0.5*1.000), n_poly=3, interaction_has_T=True))
     else:
         eta = np.nan
-    D_WC = 7.4e-8*(params['MW']*params['WC_assoc_param'])**0.5*(T)/eta/V**0.6
+    D_WC = 7.4e-8*(params['MW']*params['WC_assoc_param'])**0.5*T/eta/V**0.6
     return D_WC, eta, params['MW']
 
 #### add Wilke-Chang