High-throughput computational screening of nanoporous adsorbents for CO 2 capture from natural gas

Authors: Efrem Braun1, Alexander F. Zurhelle1,2, Wouter Thijssen1, Sondre Schnell1,3, Li-Chiang Lin1,4, Jihan Kim5, Joshua A. Thompson6, Berend Smit1,7,8,9*

  1. Department of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, CA 94720, USA
  2. Department of Chemistry, RWTH Aachen University, Templergraben 55, D-52056 Aachen, Germany
  3. Department of Chemistry, Norwegian University of Science and Technology, 7491 Trondheim, Norway
  4. Department of Process and Energy, Delft University of Technology, Leeghwaterstraat 39, 2628 CB Delft, The Netherlands
  5. Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 305-338, Republic of Korea
  6. Chevron USA Inc., 100 Chevron Way, Richmond, CA 94801, USA
  7. Department of Chemistry, University of California, Berkeley, Berkeley, CA 94720, USA
  8. Materials Science Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
  9. Institut des Sciences et Ingénierie Chimiques (ISIC), Valais, École Polytechnique Fédérale de Lausanne (EPFL), Rue de l'Industrie 17, CH-1951 Sion, Switzerland
  • Corresponding author email: berend-smit@berkeley.edu

DOI10.24435/materialscloud:2018.0005/v2 (version v2, submitted on 14 November 2018)

How to cite this entry

Efrem Braun, Alexander F. Zurhelle, Wouter Thijssen, Sondre Schnell, Li-Chiang Lin, Jihan Kim, Joshua A. Thompson, Berend Smit, High-throughput computational screening of nanoporous adsorbents for CO 2 capture from natural gas, Materials Cloud Archive (2018), doi: 10.24435/materialscloud:2018.0005/v2.


With the growth of natural gas as an energy source, upgrading CO2-contaminated supplies has become increasingly important. Here we develop a single metric that captures how well an adsorbent performs the separation of CH4 and CO2, and we then use this metric to computationally screen tens of thousands of all-silica zeolites. We show that the most important predictors of separation performance are the CO2 heat of adsorption (Qst, CO2) and the CO2 saturation loading capacity. We find that a higher-performing material results when the absolute value of the CH4 heat of adsorption (Qst, CH4) is decreased independently of Qst, CO2, but a correlation that exists between Qst, CH4 and Qst, CO2 in all-silica zeolites leads to incongruity between the objectives of optimizing Qst, CO2 and minimizing Qst, CH4, rendering Qst, CH4 nonpredictive of separation performance. We also conduct a large-scale analysis of ideal adsorbed solution theory (IAST) by comparing results obtained using directly-generated mixture isotherms to those obtained using IAST; IAST appears adequate for the purposes of establishing performance trends and structure–property relationships in a high-throughput manner, but it must be tested for validity when analyzing individual adsorbents in detail since it can produce significant errors for materials in which there is site segregation of the adsorbate species.

Version 2 provides the structures in CIF format.

Materials Cloud sections using this data

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File name Size Description
MD5MD5: 5a4e49b07b68ede01f243cf3adc91fe9
114.7 KiB IZA zeolite structures (CIF format)
MD5MD5: f5798d644b86da5d723acb035bb32a8b
160.8 KiB IZA zeolite structures (CSSR format)
MD5MD5: 02049f4f5fadb1edcf6785b1b457c34f
97.0 MiB hypothetical PCOD zeolite structures (CIF format)
MD5MD5: a1efe80fa0d3efdc4f17eb762b28a98a
136.4 MiB hypothetical PCOD zeolite structures (CSSR format)


Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.


3D three-dimensional database high-throughput porous polymer networks IZA nanoporous methane storage deliverable capacities DC zeolites grand canonical Monte Carlo GCMC

Version history

14 November 2018 [This version]

15 May 2018