@article{article_653429, title={Grand Canonical Monte Carlo Modeling of Anesthetic Xe Separation from Exhale Gas Mixtures Using Metal Organic Frameworks}, journal={Erzincan University Journal of Science and Technology}, volume={12}, pages={1705–1714}, year={2019}, DOI={10.18185/erzifbed.653429}, author={Gurdal, Yeliz}, keywords={Grand Canonical Monte Carlo Simülasyonu,metal oraganik kafes yapılar,gaz ayırma}, 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 CO 2 , Xe, and N 2 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 CO 2 for  CO 2 /Xe mixture,  Xe adsorption is favorable in the case of Xe/N 2 mixture. While SUDBOI shows significantly large CO 2 adsorption selectivity for CO 2 /Xe mixture,  ZUQPOQ has the largest adsorption selectivity for Xe/N 2 mixture.

 

}, number={3}, publisher={Erzincan Binali Yıldırım Üniversitesi} }