This record has versions v1, v2. This is version v1.

materialscloud:2018.0005/v1

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

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 authors emails: Berend-Smit@berkeley.edu
DOI10.24435/materialscloud:2018.0005/v1 [version v1]

Publication date: May 15, 2018

How to cite this record

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 2018.0005/v1 (2018), doi: 10.24435/materialscloud:2018.0005/v1.

Description

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.

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Files

File name Size Description
EFREM_IZA.tar.gz
MD5md5:f5ce73a09e80160c1ab4faada081cfcf
161.5 KiB IZA zeolite structures (CSSR format)
EFREM_PCOD.tar.gz
MD5md5:5e52a6b4fe57cfcaf4ab114e5b32190c
136.6 MiB hypothetical PCOD zeolite structures (CSSR format)

License

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

Keywords

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