Grand Canonical Monte Carlo Modeling of Anesthetic Xe Separation from Exhale Gas Mixtures Using Metal Organic Frameworks

Abstract

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.