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

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    Author
    Bai, Peng
    Jeon, Mi Yeong
    Ren, Limin
    Knight, Chris
    Deem, Michael W.
    Tsapatsis, Michael
    Siepmann, J. Ilja
    Date
    2015
    Citation
    Bai, Peng, Jeon, Mi Yeong, Ren, Limin, et al.. "Discovery of optimal zeolites for challenging separations and chemical transformations using predictive materials modeling." Nature Communications, (2015) http://dx.doi.org/10.1038/ncomms6912.
    Published Version
    http://dx.doi.org/10.1038/ncomms6912
    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.
    Type
    Journal article
    Citable link to this page
    http://hdl.handle.net/1911/79024
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    • Bioengineering Publications [397]
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    • Physics and Astronomy Publications [955]

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    Managed by the Digital Scholarship Services at Fondren Library, Rice University
    Physical Address: 6100 Main Street, Houston, Texas 77005
    Mailing Address: MS-44, P.O.BOX 1892, Houston, Texas 77251-1892