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Title: Adsorption of simple square-well fluids in slit nanopores: Modeling based on Generalized van der Waals partition function and Monte Carlo simulation

Abstract

A model based on the Generalized van der Waals partition function is derived to predict the adsorption of square-well fluid in slit pores of any size, the walls of which also have square-well potential. The space inside the pore is divided into several regions based on the extent of the attractive regions generated by the walls. Closed-form expressions of the chemical potentials of the confined fluid in different regions in the pore are obtained. The densities of fluid in different regions are calculated by equalizing the chemical potentials of fluid in those regions to that of the bulk phase. To examine the accuracy of the model, the Grand Canonical Monte Carlo (GCMC) simulation is also conducted. Here we find that the model well captures the effects of the bulk conditions and the properties of adsorbate and pore on the density of adsorbate in the pore. The model is also shown to be able to predict the adsorption of real gases in various activated carbons.

Authors:
 [1];  [1]
+ Show Author Affiliations
  1. University of Wyoming, Laramie, WY (United States)
Publication Date:
Research Org.:
West Virginia Univ., Morgantown, WV (United States); Univ. of Wyoming, Laramie, WY (United States)
Sponsoring Org.:
USDOE Office of Policy and International Affairs (PO); State of Wyoming; USDOE
OSTI Identifier:
1538094
Alternate Identifier(s):
OSTI ID: 1576064
Grant/Contract Number:  
PI0000017
Resource Type:
Accepted Manuscript
Journal Name:
Chemical Engineering Science
Additional Journal Information:
Journal Volume: 177; Journal Issue: C; Journal ID: ISSN 0009-2509
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; generalized van der Waals; GCMC simulation; adsorption; slit pore; square-well potential; narrow pores

Citation Formats

Kong, Lingli, and Adidharma, Hertanto. Adsorption of simple square-well fluids in slit nanopores: Modeling based on Generalized van der Waals partition function and Monte Carlo simulation. United States: N. p., 2017. Web. doi:10.1016/j.ces.2017.11.025.
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Kong, Lingli, & Adidharma, Hertanto. Adsorption of simple square-well fluids in slit nanopores: Modeling based on Generalized van der Waals partition function and Monte Carlo simulation. United States. https://doi.org/10.1016/j.ces.2017.11.025
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Kong, Lingli, and Adidharma, Hertanto. Tue . "Adsorption of simple square-well fluids in slit nanopores: Modeling based on Generalized van der Waals partition function and Monte Carlo simulation". United States. https://doi.org/10.1016/j.ces.2017.11.025. https://www.osti.gov/servlets/purl/1538094.
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@article{osti_1538094,
title = {Adsorption of simple square-well fluids in slit nanopores: Modeling based on Generalized van der Waals partition function and Monte Carlo simulation},
author = {Kong, Lingli and Adidharma, Hertanto},
abstractNote = {A model based on the Generalized van der Waals partition function is derived to predict the adsorption of square-well fluid in slit pores of any size, the walls of which also have square-well potential. The space inside the pore is divided into several regions based on the extent of the attractive regions generated by the walls. Closed-form expressions of the chemical potentials of the confined fluid in different regions in the pore are obtained. The densities of fluid in different regions are calculated by equalizing the chemical potentials of fluid in those regions to that of the bulk phase. To examine the accuracy of the model, the Grand Canonical Monte Carlo (GCMC) simulation is also conducted. Here we find that the model well captures the effects of the bulk conditions and the properties of adsorbate and pore on the density of adsorbate in the pore. The model is also shown to be able to predict the adsorption of real gases in various activated carbons.},
doi = {10.1016/j.ces.2017.11.025},
journal = {Chemical Engineering Science},
number = C,
volume = 177,
place = {United States},
year = {Tue Nov 21 00:00:00 EST 2017},
month = {Tue Nov 21 00:00:00 EST 2017}
}
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https://doi.org/10.1016/j.ces.2017.11.025
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