Literature DB >> 33940796

Quantifying the effect of polar interactions on the behavior of binary mixtures: Phase, interfacial, and excess properties.

Ismail I I Alkhatib1, Lourdes F Vega1.   

Abstract

In this work, polar soft-Statistical Associating Fluid Theory (SAFT) was used in a systematic manner to quantify the influence of polar interactions on the phase equilibria, interfacial, and excess properties of binary mixtures. The theory was first validated with available molecular simulation data and then used to isolate the effect of polar interactions on the thermodynamic behavior of the mixtures by fixing the polar moment of one component while changing the polar moment of the second component from non-polar to either highly dipolar or quadrupolar, examining 15 different binary mixtures. It was determined that the type and magnitude of polar interactions have direct implications on the vapor-liquid equilibria (VLE), resulting in azeotropy for systems of either dipolar or quadrupolar fluids when mixed with non-polar or low polar strength fluids, while increasing the polar strength of one component shifts the VLE to be more ideal. Additionally, excess properties and interfacial properties such as interfacial tension, density profiles, and relative adsorption at the interface were also examined, establishing distinct enrichment in the case of mixtures with highly quadrupolar fluids. Finally, polar soft-SAFT was applied to describe the thermodynamic behavior of binary mixtures of experimental systems exhibiting various intermolecular interactions (non-polar and polar), not only demonstrating high accuracy and robustness through agreement with experimental data but also providing insights into the effect of polarity on the interfacial properties of the studied mixtures. This work proves the value of having an accurate theory for isolating the effect of polarity, especially for the design of ad hoc polar solvents.

Year:  2021        PMID: 33940796     DOI: 10.1063/5.0046034

Source DB:  PubMed          Journal:  J Chem Phys        ISSN: 0021-9606            Impact factor:   3.488


  2 in total

1.  Searching for Sustainable Refrigerants by Bridging Molecular Modeling with Machine Learning.

Authors:  Ismail I I Alkhatib; Carlos G Albà; Ahmad S Darwish; Fèlix Llovell; Lourdes F Vega
Journal:  Ind Eng Chem Res       Date:  2022-05-18       Impact factor: 4.326

2.  Assessment of Low Global Warming Potential Refrigerants for Drop-In Replacement by Connecting their Molecular Features to Their Performance.

Authors:  Carlos G Albà; Ismail I I Alkhatib; Fèlix Llovell; Lourdes F Vega
Journal:  ACS Sustain Chem Eng       Date:  2021-12-07       Impact factor: 8.198

  2 in total

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