Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Multicomponent adsorption in mesoporous flexible materials with flat-histogram Monte Carlo methods

Published

Author(s)

Nathan Mahynski, Vincent K. Shen

Abstract

We demonstrate an extensible flat-histogram Monte Carlo simulation methodology for studying the adsorption of multicomponent fluids in flexible porous solids. This methodology allows us to easily obtain the complete free energy landscape for the confined fluid-solid system in equilibrium with a bulk fluid of any arbitrary composition. We use this approach to study the adsorption of a prototypical coarse-grained binary fluid in “Hookean” solids, where the free energy of the solid may be described as a simple spring. However, our approach is fully extensible to solids with arbitrarily complex free energy profiles. We demonstrate that by tuning the fluid-solid interaction ranges, the inhomogeneous fluid structure inside the pore can give rise to enhanced selective capture of a larger species through cooperative adsorption with a smaller one. The maximum enhancement in selectivity is observed at low to intermediate pressures, and is especially pronounced when the larger species is very dilute in the bulk. This suggest a mechanism by which the selective capture of a minor component from a bulk fluid may be enhanced.
Citation
Journal of Chemical Physics
Volume
145
Issue
7

Keywords

confined fluid, adsorption, selectivity, multicomponent

Citation

Mahynski, N. and Shen, V. (2016), Multicomponent adsorption in mesoporous flexible materials with flat-histogram Monte Carlo methods, Journal of Chemical Physics, [online], https://doi.org/10.1063/1.4966573 (Accessed April 20, 2024)
Created November 7, 2016, Updated November 10, 2018