Issue 54, 2022, Issue in Progress

Molecular dynamics simulation of adsorption and separation of xylene isomers by Cu-HKUST-1

Abstract

Metal–organic frameworks (MOFs) are widely used in the adsorption separation of various gases. A fundamental understanding of the effective separation of xylene isomers helps improve aromatic products' separation efficiency and reduce industrial separation costs. Grand Canonical Monte Carlo (GCMC) simulations combined with Molecular Science is widely used to predict gas adsorption and diffusion in single crystals with metal–organic frameworks. We performed a GCMC + MD combined approach to study xylene isomers' adsorption and separation in Cu-HKUST-1 to predict the permeability and selectivity of the ternary gas mixture in the MOF with the adsorption and diffusion usage data. Most current studies take into account the computational cost and difficulty. Most recent research models are limited to the adsorption of a single or specific molecule, such as hydrogen, methane, carbon dioxide, etc. For this reason, we report an attempt to study the adsorption separation of aromatic gases (p-xylene/o-xylene/m-xylene) based on Cu-HKUST-1 single-crystal materials based on some previous research methods with an appropriate increase in computational cost. To predict the adsorption selectivity and permeability of the ternary mixture of xylene isomers on the MOF surface, the model simulation calculates key parameters of gas adsorption, including gas adsorption volume (N), the heat of adsorption (Qst), Henry coefficient (K), and diffusion coefficient (D).

Graphical abstract: Molecular dynamics simulation of adsorption and separation of xylene isomers by Cu-HKUST-1

Supplementary files

Article information

Article type
Paper
Submitted
31 Oct 2022
Accepted
04 Dec 2022
First published
09 Dec 2022
This article is Open Access
Creative Commons BY-NC license

RSC Adv., 2022,12, 35290-35299

Molecular dynamics simulation of adsorption and separation of xylene isomers by Cu-HKUST-1

G. Ji, T. Xiang, X. Zhou, L. Chen, Z. Zhang, B. Lu and X. Zhou, RSC Adv., 2022, 12, 35290 DOI: 10.1039/D2RA06873G

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