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Understanding CO adsorption in MOFs combining atomic simulations and machine learning

dc.contributor.departmentDepartment of Chemical and Biological Engineering
dc.contributor.kuauthorKeskin, Seda
dc.contributor.kuauthorErçakır, Göktuğ
dc.contributor.kuauthorAksu, Gökhan Önder
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.date.accessioned2025-03-06T21:00:28Z
dc.date.issued2024
dc.description.abstractCorrection to: Scientific Reportshttps://doi.org/10.1038/s41598-024-76491-x, published online 22 October 2024 In the original version of this article, one reference was omitted from the Reference list. Reference 25 “Naderlou, S., Vahedpour, M., and Franz, D. M. Exploring the Role of Functional Groups in Modulating NO and CO Adsorption and Diffusion in 2D (Zn)MOF-470: A Multiscale Computational Study. The Journal of Physical Chemistry C 2023 127 (38), 19301-19323 DOI: 10.1021/acs.jpcc.3c05371.” As a result, the Introduction “Franz and coworkers focused on CO adsorption in Zn-MOF-470 and its functionalized versions using GCMC simulations https://pubs.acs.org/doi/10.1021/acs.jpcc.3c05371. Results revealed that hydroxyl (-OH) functionalized Zn-MOF-470 achieves the highest uptake 116.2 cm3/g (~ 5.18 mol/kg) outperforming others at 1 bar, 298 K.” now reads, “Franz and coworkers focused on CO adsorption in Zn-MOF-470 and its functionalized versions using GCMC simulations25. Results revealed that hydroxyl (-OH) functionalized Zn-MOF-470 achieves the highest uptake at 116.2 cm3/g (~ 5.18 mol/kg) outperforming others at 1 bar, 298 K.” The original Article has been corrected.
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.indexedbyPubMed
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.identifier.doi10.1038/s41598-024-79664-w
dc.identifier.issn20452322
dc.identifier.issue1
dc.identifier.quartileQ1
dc.identifier.scopus2-s2.0-85209553169
dc.identifier.urihttps://doi.org/10.1038/s41598-024-79664-w
dc.identifier.urihttps://hdl.handle.net/20.500.14288/27897
dc.identifier.volume14
dc.identifier.wos1359478400036
dc.keywordsCO adsorption
dc.keywordsMetal-organic frameworks (MOFs)
dc.keywordsAtomic simulations
dc.keywordsMachine learning
dc.keywordsAdsorption mechanisms
dc.keywordsComputational chemistry
dc.keywordsMolecular modeling
dc.keywordsPorous materials
dc.keywordsGas separation
dc.keywordsMaterial design
dc.language.isoeng
dc.publisherNature Portfolio
dc.relation.ispartofScientific Reports
dc.subjectMultidisciplinary sciences
dc.titleUnderstanding CO adsorption in MOFs combining atomic simulations and machine learning
dc.typeOther
dc.type.otherCorrection
dspace.entity.typePublication
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