Prediction of Composition-Dependent Self-Diffusion Coefficients in Binary Liquid Mixtures: The Missing Link for Darken-Based Models
| Autor: | Ludger Wolff, Thijs J. H. Vlugt, Seyed Hossein Jamali, Othonas A. Moultos, Tim M. Becker, André Bardow |
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| Rok vydání: | 2018 |
| Předmět: |
Work (thermodynamics)
Self-diffusion Materials science General Chemical Engineering Binary number Thermodynamics 02 engineering and technology General Chemistry Function (mathematics) Composition (combinatorics) 021001 nanoscience & nanotechnology Industrial and Manufacturing Engineering Dilution Molecular dynamics 020401 chemical engineering 0204 chemical engineering Diffusion (business) 0210 nano-technology |
| Zdroj: | Industrial and Engineering Chemistry Research, 57(43) |
| ISSN: | 1520-5045 0888-5885 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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