Grand Canonical Monte Carlo Modeling of Anesthetic Xe Separation from Exhale Gas Mixtures Using Metal Organic Frameworks
Abstract
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Xe
has been shown to be a promising candidate for anesthetic applications.
However, its high price prevents its
usage in clinical industry. An alternative approach is to recover Xe from
anesthetic exhale gas mixture and recycle it to the inhale gas stream.
Although, many membranes and/or adsorbents have been proposed for recovering
anesthetic Xe, using metal organic frameworks (MOFs) for adsorption based
separation of anesthetic Xe exhale gas
mixtures has been newly studied. MOFs have
tunable pore sizes, large surface areas, and high porosities which
make them potential candidates for gas separation applications. Currently,
very little is known about anesthetic Xe recovery performances of MOFs. We theoretically
investigate adsorption based separation of single component and binary
mixtures of CO2, Xe, and N2 in three MOFs, namely CECYOY, SUDBOI, and ZUQPOQ. Single
component and binary adsorption isotherms and adsorption selectivities are
calculated using Grand Canonical Monte Carlo simulations for each MOF in
order to characterize their performances as adsorbents. Results suggest that
while MOFs prefer adsorption of CO2 for CO2/Xe
mixture, Xe adsorption is favorable in
the case of Xe/N2 mixture. While SUDBOI shows significantly large
CO2 adsorption selectivity for CO2/Xe mixture, ZUQPOQ has the largest adsorption
selectivity for Xe/N2 mixture.
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Keywords
Thanks
References
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Details
Primary Language
English
Subjects
Engineering
Journal Section
Research Article
Authors
Yeliz Gurdal
*
0000-0002-6245-891X
Türkiye
Publication Date
December 31, 2019
Submission Date
November 30, 2019
Acceptance Date
December 24, 2019
Published in Issue
Year 2019 Volume: 12 Number: 3