Entropy Scaling for Diffusion Coefficients in Fluid Mixtures

TL;DR

This paper introduces an entropy scaling framework for accurately predicting both self and mutual diffusion coefficients in fluid mixtures across various states, using thermodynamically consistent data.

Contribution

It develops a novel entropy scaling method for mixture diffusion coefficients, integrating pure component data and mixture entropy from equations of state.

Findings

Framework predicts diffusion coefficients over wide temperature and pressure ranges.

Method works for gaseous, liquid, supercritical, and metastable states.

Enables accurate modeling of strongly non-ideal mixtures.

Abstract

Entropy scaling is a powerful technique that has been used for predicting transport properties of pure components over a wide range of states. However, modeling mixture diffusion coefficients by entropy scaling is an unresolved task. We tackle this issue and present an entropy scaling framework for predicting mixture self-diffusion coefficients as well as mutual diffusion coefficients in a thermodynamically consistent way. The predictions of the mixture diffusion coefficients are made based on information on the self-diffusion coefficients of the pure components and the infinite-dilution diffusion coefficients. This is accomplished using information on the entropy of the mixture, which is taken here from molecular-based equations of state. Examples for the application of the entropy scaling framework for the prediction of diffusion coefficients in mixtures illustrate its performance. It enables predictions over a wide range of temperatures and pressures including gaseous, liquid, supercritical, and metastable states - also for strongly non-ideal mixtures.