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

Peng Bai, Mi Young Jeon, Limin Ren, Chris Knight, Michael W. Deem, Michael Tsapatsis, J. Ilja Siepmann

Research output: Contribution to journalArticlepeer-review

65 Scopus citations

Abstract

Zeolites 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.

Original languageEnglish (US)
Article number5912
JournalNature communications
Volume6
DOIs
StatePublished - Jan 21 2015

Bibliographical note

Funding Information:
Financial support from the Department of Energy Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences under Award DE-FG02– 12ER16362 is gratefully acknowledged. This research used resources of the Argonne Leadership Computing Facility (ALCF) at Argonne National Laboratory, which is supported by the Office of Science of the Department of Energy under contract DE-AC02– 06CH11357. Additional computer resources were provided by the Minnesota Supercomputing Institute.

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