Prediction of activity coefficients in water-methanol mixtures using a generalized Debye-Huckel model

TL;DR

This paper introduces a generalized Debye-Huckel model based on Poisson-Fermi theory to accurately predict electrolyte activity coefficients in water-methanol mixtures across all compositions, accounting for complex interactions and particle effects.

Contribution

The paper develops a new generalized Debye-Huckel model with only 3 empirical parameters for fitting and predicting electrolyte activities in mixed solvents, with an automatic parameter optimization algorithm.

Findings

Successfully fits experimental data for NaF, NaCl, NaBr in pure water.

Accurately predicts activity coefficients in water-methanol mixtures for any methanol percentage.

Provides a physically meaningful interpretation of model parameters.

Abstract

We propose a generalized Debye-Huckel model from Poisson-Fermi theory to predict the mean activity coefficient of electrolytes in water-methanol mixtures with arbitrary percentage of methanol from 0 to 100%. The model applies to any number of ionic species and accounts for both short and long ion-ion, ion-water, ion-methanol, and water-methanol interactions, the size effect of all particles, and the dielectric effect of mixed-solvent solutions. We also present a numerical algorithm with mathematical and physical details for using the model to fit or predict experimental data. The model has only 3 empirical parameters to fit the experimental data of NaF, NaCl, and NaBr, for example, in pure-water solutions. It then uses another 3 parameters to predict the activities of these salts in mixed-solvent solutions for any percentage of methanol. Values of these parameters show mathematical or physical meaning of ionic activities under variable mixing condition and salt concentration. The algorithm can automatically determine optimal values for the 3 fitting parameters without any manual adjustments.