Surface tension as a function of temperature and composition for a broad range of mixtures
Nadia Shardt, Yingnan Wang, Zhehui Jin, Janet A.W. Elliott
Abstract
It is desirable to predict the surface tension of liquid mixtures for a wide range of compositions, temperatures, and pressures, but current state-of-the-art calculations (e.g., density gradient theory) are computationally expensive. We propose a computationally simple—but accurate—semi-empirical mathematical model of surface tension for a wide variety of multicomponent mixtures, including those with a supercritical compound when coupled with an equation of state (by introducing a reduced mole fraction scaled by a critical composition). Our predictions for binary systems with one supercritical component are an average of 0.22 mN/m away from literature experimental data (466 data points), and those for systems with two subcritical components (293–333 K) are within 0.09 mN/m (236 data points). We make predictions for methanol + ethanol + water using binary coefficients within an average of 0.71 mN/m (196 data points). Given its computational simplicity and wide applicability, the proposed model will be useful for many applications.