Publication:
Finding high-performance MOFs for effective SF6/N2 separation through high-throughput computational screening and machine learning

dc.contributor.departmentDepartment of Chemical and Biological Engineering
dc.contributor.departmentGraduate School of Sciences and Engineering
dc.contributor.kuauthorSezgin, Pelin
dc.contributor.kuauthorGülbalkan, Hasan Can
dc.contributor.kuauthorKeskin, Seda
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.contributor.schoolcollegeinstituteGRADUATE SCHOOL OF SCIENCES AND ENGINEERING
dc.date.accessioned2025-03-06T20:59:21Z
dc.date.issued2024
dc.description.abstractGiven the rapidly expanding pool of synthesized and hypothetical metal-organic frameworks (MOFs), testing every single material for SF6/N-2 separation by iterative experimental methods or computationally demanding molecular simulations is not practical. In this study, we integrated high-throughput computational screening and machine learning (ML) approaches to evaluate SF6/N-2 mixture adsorption and separation performances of over 25 000 different types of synthesized and hypothetical MOFs (hypoMOFs), representing the largest set of structures studied for SF6/N-2 separation to date. SF6/N-2 mixture adsorption data that we produced for synthesized MOFs using molecular simulations were utilized to develop ML models to accurately and quickly predict SF6 and N-2 uptakes, SF6/N-2 selectivities, SF6 working capacities, adsorbent performance scores, and regenerabilities of both synthesized and hypoMOFs. Results showed the MOF space that we studied exhibits very high SF6/N-2 selectivities in the range of 1.8-4204 at 1 bar in addition to high SF6 working capacities between 0.04-5.68 mol kg(-1) at an adsorption pressure of 1 bar and desorption pressure of 0.1 bar at room temperature. The top-performing MOF adsorbents for SF6/N-2 mixture separation were identified to have Zn, Cu, Ni metals;terphenyl, pyridine, naphthalene linkers;and medium pore sizes. Our comprehensive computational approach offers a highly efficient alternative to brute-force computer simulations by enabling the rapid assessment of the MOF adsorbents for SF6/N-2 separation and provides molecular insights into the key structural features of the most promising adsorbents.
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuEU
dc.description.sponsorshipS K acknowledges funding by the European Union (ERC, STARLET, 101124002). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them.
dc.identifier.doi10.1088/2515-7639/ad80cd
dc.identifier.eissn2515-7639
dc.identifier.grantnoHORIZON EUROPE European Research Councilhttps://doi.org/10.13039/100019180 [101124002];European Union (ERC)
dc.identifier.issue4
dc.identifier.quartileQ2
dc.identifier.scopus2-s2.0-85207258503
dc.identifier.urihttps://doi.org/10.1088/2515-7639/ad80cd
dc.identifier.urihttps://hdl.handle.net/20.500.14288/27664
dc.identifier.volume7
dc.identifier.wos1336996600001
dc.keywordsMOF
dc.keywordsMolecular simulation
dc.keywordsGas separation
dc.keywordsMachine learning
dc.language.isoeng
dc.publisherIOP Publishing Ltd
dc.relation.ispartofJournal of Physics: Materials
dc.subjectMaterials science, multidisciplinary
dc.titleFinding high-performance MOFs for effective SF6/N2 separation through high-throughput computational screening and machine learning
dc.typeJournal Article
dspace.entity.typePublication
local.publication.orgunit1GRADUATE SCHOOL OF SCIENCES AND ENGINEERING
local.publication.orgunit1College of Engineering
local.publication.orgunit2Department of Chemical and Biological Engineering
local.publication.orgunit2Graduate School of Sciences and Engineering
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