Prediction of Composition-Dependent Self-Diffusion Coefficients in Binary Liquid Mixtures: The Missing Link for Darken-Based Models

• Published in: Industrial & engineering chemistry research Vol. 57; no. 43; pp. 14784 - 14794
• Main Authors: Wolff, Ludger, Jamali, Seyed Hossein, Becker, Tim M, Moultos, Othonas A, Vlugt, Thijs J. H, Bardow, André
• Format: Journal Article
• Language: English
• Published: American Chemical Society 31.10.2018
• ISSN: 0888-5885; 1520-5045
• DOI: 10.1021/acs.iecr.8b03203

Mutual diffusion coefficients can be successfully predicted with models based on the Darken equation. However, Darken-based models require composition-dependent self-diffusion coefficients which are rarely available. In this work, we present a predictive model for composition-dependent self-diffusion coefficients (also called tracer diffusion coefficients or intradiffusion coefficients) in nonideal binary liquid mixtures. The model is derived from molecular dynamics simulation data of Lennard-Jones systems. A strong correlation between nonideal diffusion effects and the thermodynamic factor is observed. We extend the model by McCarty and Mason ( Phys. Fluids 1960, 3, 908−922 ) for ideal binary gas mixtures to predict the composition-dependent self-diffusion coefficients in nonideal binary liquid mixtures. Our new model is a function of the thermodynamic factor, the self-diffusion coefficients at infinite dilution, and the self-diffusion coefficients of the pure substances, which are readily available. We validate our model with experimental data of 9 systems. For both Lennard-Jones systems and experimental data, the accuracy of the predicted self-diffusion coefficients is improved by a factor of 2 compared to the correlation of McCarty and Mason. Thus, our new model significantly expands the practical applicability of Darken-based models for the prediction of mutual diffusion coefficients.