Publication: Finding high-performance MOFs for effective SF6/N2 separation through high-throughput computational screening and machine learning
dc.contributor.department | Department of Chemical and Biological Engineering | |
dc.contributor.department | Graduate School of Sciences and Engineering | |
dc.contributor.kuauthor | Sezgin, Pelin | |
dc.contributor.kuauthor | Gülbalkan, Hasan Can | |
dc.contributor.kuauthor | Keskin, Seda | |
dc.contributor.schoolcollegeinstitute | College of Engineering | |
dc.contributor.schoolcollegeinstitute | GRADUATE SCHOOL OF SCIENCES AND ENGINEERING | |
dc.date.accessioned | 2025-03-06T20:59:21Z | |
dc.date.issued | 2024 | |
dc.description.abstract | Given 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.indexedby | WOS | |
dc.description.indexedby | Scopus | |
dc.description.publisherscope | International | |
dc.description.sponsoredbyTubitakEu | EU | |
dc.description.sponsorship | S 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.doi | 10.1088/2515-7639/ad80cd | |
dc.identifier.eissn | 2515-7639 | |
dc.identifier.grantno | HORIZON EUROPE European Research Councilhttps://doi.org/10.13039/100019180 [101124002];European Union (ERC) | |
dc.identifier.issue | 4 | |
dc.identifier.quartile | Q2 | |
dc.identifier.scopus | 2-s2.0-85207258503 | |
dc.identifier.uri | https://doi.org/10.1088/2515-7639/ad80cd | |
dc.identifier.uri | https://hdl.handle.net/20.500.14288/27664 | |
dc.identifier.volume | 7 | |
dc.identifier.wos | 1336996600001 | |
dc.keywords | MOF | |
dc.keywords | Molecular simulation | |
dc.keywords | Gas separation | |
dc.keywords | Machine learning | |
dc.language.iso | eng | |
dc.publisher | IOP Publishing Ltd | |
dc.relation.ispartof | Journal of Physics: Materials | |
dc.subject | Materials science, multidisciplinary | |
dc.title | Finding high-performance MOFs for effective SF6/N2 separation through high-throughput computational screening and machine learning | |
dc.type | Journal Article | |
dspace.entity.type | Publication | |
local.publication.orgunit1 | GRADUATE SCHOOL OF SCIENCES AND ENGINEERING | |
local.publication.orgunit1 | College of Engineering | |
local.publication.orgunit2 | Department of Chemical and Biological Engineering | |
local.publication.orgunit2 | Graduate School of Sciences and Engineering | |
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