Potential of polarizable force fields for predicting the separation performance of small hydrocarbons in M-MOF-74
Tim M. Becker (TU Delft - Engineering Thermodynamics)
Azahara Luna-Triguero (University Pablo de Olavide)
Jose Manuel Vicent-Luna (University Pablo de Olavide)
Li Chiang Lin (The Ohio State University)
David Dubbeldam (Universiteit van Amsterdam)
Sofia Calero (University Pablo de Olavide)
Thijs J.H. Vlugt (TU Delft - Engineering Thermodynamics)
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Abstract
The separation of light olefins from paraffins via cryogenic distillation is a very energy intensive process. Solid adsorbents and especially metal-organic frameworks with open metal sites have the potential to significantly lower the required energy. Specifically, M-MOF-74 has drawn considerable attention for application in olefin/paraffin separation. To investigate how the separation proceeds on a molecular level and to design better materials, molecular simulation can be a useful tool. Unfortunately, it is still a challenge to model the adsorption behavior of many adsorbates in metal-organic frameworks with open metal sites. Previously, the inclusion of explicit polarization has been suggested to improve the quality of classical force fields for such systems. Here, the potential of polarizable force fields for the description of olefins and paraffins in metal-organic frameworks with open metal sites is investigated. In particular, heats of adsorption, binding geometries, and adsorption isotherms are calculated for C2H4, C2H6, C3H6, and C3H8 in M-MOF-74 (with M = Co, Mn, Fe, and Ni). In this study, no force field parameters are adjusted to improve the model. The results show that including explicit polarization significantly improves the description of the adsorption in comparison to non-polarizable generic force fields which do not consider explicit polarization. The study also reveals that simulation predictions are sensitive to the assigned repulsive potential and framework charges. A fully re-parametrized polarizable force field may have the capability to improve the predictions even further.