What Are the Best Materials To Separate a Xenon/Krypton Mixture?

Simon, Cory M.; Mercado, Rocio; Schnell, Sondre K.; Smit, Berend; Haranczyk, Maciej

doi:10.1021/acs.chemmater.5b01475

Title: What Are the Best Materials To Separate a Xenon/Krypton Mixture?

Journal Article · Tue Jun 09 00:00:00 EDT 2015 · Chemistry of Materials

DOI:https://doi.org/10.1021/acs.chemmater.5b01475· OSTI ID:1474401

Simon, Cory M. ^[1]; Mercado, Rocio ^[2]; Schnell, Sondre K. ^[3]; Smit, Berend ^[1]; Haranczyk, Maciej ^[4]

Univ. of California, Berkeley, CA (United States). Dept. of Chemical and Biomolecular Engineering
Univ. of California, Berkeley, CA (United States). Dept. of Chemistry
Univ. of California, Berkeley, CA (United States). Dept. of Chemical and Biomolecular Engineering; Norwegian Univ. of Science and Technology, Trondheim (Norway)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Scientific Computing Group

Accelerating progress in the discovery and deployment of advanced nanoporous materials relies on chemical insight and structure property relationships for rational design. Because of the complexity of this problem, trial-and-error is heavily involved in the laboratory today. A cost-effective route to aid experimental materials discovery is to construct structure models of nanoporous materials in silico and use molecular simulations to rapidly test them and elucidate data-driven guidelines for rational design. For example, highly-tunable nanoporous materials have shown promise as adsorbents for separating an industrially relevant gaseous mixture of xenon and krypton. Here in this work, we characterize, screen, and analyze the Nanoporous Materials Genome, a database of ca. 670,000 porous material structures, for candidate adsorbents for xenon/krypton separations. For over half a million structures, the computational resources required for a brute-force screening using grand-canonical Monte Carlo simulations of Xe/Kr adsorption are prohibitive. To overcome the computational cost, we used a hybrid approach combining machine learning algorithms (random forests) with molecular simulations. We compared the results from our large-scale screening with simple pore models to rationalize the strong link between pore size and selectivity. With this insight, we then analyzed the anatomy of the binding sites of the most selective materials. These binding sites can be constructed from tubes, pockets, rings, or cages and are often composed of non-discrete chemical fragments. The complexity of these binding sites emphasizes the importance of high-throughput computational screenings to discover new materials. Interestingly, our screening study predicts that the two most selective materials in the database are an aluminophosphate zeolite analogue and a calcium based coordination network, both of which have already been synthesized but not yet tested for Xe/Kr separations.

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Research Organization:: Univ. of Minnesota, Minneapolis, MN (United States). Nanoporous Materials Genome Center; Energy Frontier Research Centers (EFRC) (United States). Center for Gas Separations Relevant to Clean Energy Technologies (CGS)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Contributing Organization:: National Energy Research Scientific Computing Center

Grant/Contract Number:: FG02-12ER16362; SC0008688

OSTI ID:: 1474401

Journal Information:: Chemistry of Materials, Vol. 27, Issue 12; ISSN 0897-4756

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 183 works

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Metal–organic framework with optimally selective xenon adsorption and separation Banerjee, Debasis; Simon, Cory M.; Plonka, Anna M. Nature Communications, Vol. 7, Issue 1 https://doi.org/10.1038/ncomms11831	journal	June 2016
Deep neural network learning of complex binary sorption equilibria from molecular simulation data Sun, Yangzesheng; DeJaco, Robert F.; Siepmann, J. Ilja Chemical Science, Vol. 10, Issue 16 https://doi.org/10.1039/c8sc05340e	journal	January 2019
XRD-DSC: a screening tool for identifying effective MOFs for selective gas sorption from humid gas streams Parise, John B.; Chen, Xianyin; Plonka, Anna M. Powder Diffraction, Vol. 34, Issue 1 https://doi.org/10.1017/s088571561900006x	journal	February 2019
An In-Situ Neutron Diffraction and DFT Study of Hydrogen Adsorption in a Sodalite-Type Metal-Organic Framework, Cu-BTTri : An Asgari, Mehrdad; Semino, Rocio; Schouwink, Pascal European Journal of Inorganic Chemistry, Vol. 2019, Issue 8 https://doi.org/10.1002/ejic.201801253	journal	January 2019
Molecular modelling and machine learning for high-throughput screening of metal-organic frameworks for hydrogen storage Bobbitt, N. Scott; Snurr, Randall Q. Molecular Simulation, Vol. 45, Issue 14-15 https://doi.org/10.1080/08927022.2019.1597271	journal	April 2019
High-throughput computational screening of nanoporous adsorbents for CO ₂ capture from natural gas Braun, Efrem; Zurhelle, Alexander F.; Thijssen, Wouter Molecular Systems Design & Engineering, Vol. 1, Issue 2 https://doi.org/10.1039/c6me00043f	journal	January 2016
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High-Throughput Screening Approach for Nanoporous Materials Genome Using Topological Data Analysis: Application to Zeolites Lee, Yongjin; Barthel, Senja D.; Dłotko, Paweł Journal of Chemical Theory and Computation, Vol. 14, Issue 8 https://doi.org/10.1021/acs.jctc.8b00253	journal	June 2018
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Metal Substitution as the Method of Modifying Electronic Structure of Metal–Organic Frameworks Syzgantseva, Maria A.; Ireland, Christopher Patrick; Ebrahim, Fatmah Mish Journal of the American Chemical Society, Vol. 141, Issue 15 https://doi.org/10.1021/jacs.8b13667	journal	March 2019
Excavating hidden adsorption sites in metal-organic frameworks using rational defect engineering Chong, Sanggyu; Thiele, Günther; Kim, Jihan Nature Communications, Vol. 8, Issue 1 https://doi.org/10.1038/s41467-017-01478-4	journal	November 2017

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