Closed loops of liquid–liquid immiscibility predicted by semi-empirical cubic equations of state and classical mixing rules
文献信息
Ilya Polishuk, Jaime Wisniak, Hugo Segura
The present study demonstrates that semi-empirical cubic EOSs, which include temperature functionalities such as that of Soave and van der Waals classical mixing rules, predict the behavior around the mathematical double points of the second kind. For the first time these results allow one to fit Type VI behavior in real water solutions, that include both first and second hypercritical points, or the absence of each of these points. Although the predicted data match the experimental data, their genesis is explained by fundamental numerical pitfalls, namely non-physical and non-differentiable breaking points that are characteristic for several empirical temperature functionalities. Hence, such results cannot represent the physical nature of the real systems. Although these pitfalls may take place outside the range of practical significance for pure compounds, they may strongly affect the predictions of the mixtures even at ordinary conditions. Therefore it is shown once again that all parts of the thermodynamic phase space predicted by the EOSs are closely inter-related.
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Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.

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