Discovery of optimal zeolites for challenging separations and chemical transformations using predictive materials modeling

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dc.citation.journalTitleNature Communicationsen_US
dc.citation.volumeNumber6en_US
dc.contributor.authorBai, Pengen_US
dc.contributor.authorJeon, Mi Yeongen_US
dc.contributor.authorRen, Liminen_US
dc.contributor.authorKnight, Chrisen_US
dc.contributor.authorDeem, Michael W.en_US
dc.contributor.authorTsapatsis, Michaelen_US
dc.contributor.authorSiepmann, J. Iljaen_US
dc.date.accessioned2015-02-11T15:45:09Zen_US
dc.date.available2015-02-11T15:45:09Zen_US
dc.date.issued2015en_US
dc.description.abstractZeolites play numerous important roles in modern petroleum refineries and have the potential to advance the production of fuels and chemical feedstocks from renewable resources. The performance of a zeolite as separation medium and catalyst depends on its framework structure. To date, 213 framework types have been synthesized and >330,000 thermodynamically accessible zeolite structures have been predicted. Hence, identification of optimal zeolites for a given application from the large pool of candidate structures is attractive for accelerating the pace of materials discovery. Here we identify, through a large-scale, multi-step computational screening process, promising zeolite structures for two energy-related applications: the purification of ?ethanol from fermentation broths and the hydroisomerization of alkanes with 18-30 carbon atoms encountered in petroleum refining. These results demonstrate that predictive modelling and data-driven science can now be applied to solve some of the most challenging separation problems involving highly non-ideal mixtures and highly articulated compounds.en_US
dc.identifier.citationBai, Peng, Jeon, Mi Yeong, Ren, Limin, et al.. "Discovery of optimal zeolites for challenging separations and chemical transformations using predictive materials modeling." <i>Nature Communications,</i> 6, (2015) Nature Publishing Group: http://dx.doi.org/10.1038/ncomms6912.en_US
dc.identifier.doihttp://dx.doi.org/10.1038/ncomms6912en_US
dc.identifier.urihttps://hdl.handle.net/1911/79024en_US
dc.language.isoengen_US
dc.publisherNature Publishing Groupen_US
dc.rightsThis is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Nature Communications Group.en_US
dc.titleDiscovery of optimal zeolites for challenging separations and chemical transformations using predictive materials modelingen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpost-printen_US
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