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Summary
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This study introduces a new entropy scaling framework to predict mixture diffusion coefficients. This method accurately models diffusion in various states, including non-ideal mixtures.

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Area of Science:

  • Thermodynamics
  • Transport Phenomena
  • Physical Chemistry

Background:

  • Entropy scaling is effective for predicting pure component transport properties.
  • Predicting mixture diffusion coefficients using entropy scaling remains a challenge.

Purpose of the Study:

  • To develop a thermodynamically consistent entropy scaling framework for mixture diffusion coefficients.
  • To enable predictions using pure component self-diffusion and infinite-dilution diffusion coefficients.

Main Methods:

  • Utilizing an entropy scaling framework based on mixture entropy from molecular-based equations of state.
  • Predicting mixture self-diffusion and mutual diffusion coefficients.

Main Results:

  • The framework successfully predicts mixture diffusion coefficients.
  • Accurate predictions are achieved across gaseous, liquid, supercritical, and metastable states.
  • The method performs well even for strongly non-ideal mixtures.

Conclusions:

  • The proposed entropy scaling framework is a viable method for predicting mixture diffusion coefficients.
  • This approach extends the applicability of entropy scaling to complex mixture systems.
  • It offers a consistent way to model diffusion across diverse thermodynamic conditions.