Schmitt Sebastian, Hasse Hans, Stephan Simon
Laboratory of Engineering Thermodynamics (LTD), RPTU Kaiserslautern, Kaiserslautern, Germany.
Nat Commun. 2025 Mar 17;16(1):2611. doi: 10.1038/s41467-025-57780-z.
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.
熵标度是一种强大的技术，已被用于预测各种状态下纯组分的传输性质。然而，用熵标度对混合物扩散系数进行建模仍是一项未解决的任务。我们解决了这个问题，并提出了一个熵标度框架，以热力学一致的方式预测混合物的自扩散系数和互扩散系数。混合物扩散系数的预测是基于纯组分的自扩散系数和无限稀释扩散系数的信息。这是通过使用混合物熵的信息来实现的，这里的混合物熵取自基于分子的状态方程。熵标度框架在预测混合物扩散系数方面的应用示例说明了其性能。它能够在很宽的温度和压力范围内进行预测，包括气态、液态、超临界和亚稳态，对于强非理想混合物也是如此。
