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

• Published in: Nature communications Vol. 6; no. 1; p. 5912
• Main Authors: Bai, Peng, Jeon, Mi Young, Ren, Limin, Knight, Chris, Deem, Michael W, Tsapatsis, Michael, Siepmann, J Ilja
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 21.01.2015
• Subjects: MATERIALS SCIENCE
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/ncomms6912

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.