Data-driven design of metal–organic frameworks for wet flue gas CO2 capture
Open accessAbstract
Limiting the increase of CO 2 in the atmosphere is one of the largest challenges of our generation 1. Because carbon capture and storage is one of the few viable technologies that can mitigate current CO 2 emissions 2, much effort is focused on developing solid adsorbents that can efficiently capture CO 2 from flue gases emitted from anthropogenic sources 3. One class of materials that has attracted considerable interest in this context is metal-organic frameworks (MOFs), in which the careful combination of organic ligands with metal-ion nodes can, in principle, give rise to innumerable structurally and chemically distinct nanoporous MOFs. However, many MOFs that are optimized for the separation of CO 2 from nitrogen 4-7 do not perform well when using realistic flue gas that contains water, because water competes with CO 2 for the same adsorption sites and thereby causes the materials to lose their selectivity. Although flue gases can be dried, this renders the capture process prohibitively expensive 8,9. Here we show that data mining of a computational screening library of over 300,000 MOFs can identify different classes of strong CO 2-binding sites-which we term 'adsorbaphores'-that endow MOFs with CO 2/N 2 selectivity that persists in wet flue gases. We subsequently synthesized two water-stable MOFs containing the most hydrophobic adsorbaphore, and found that their carbon-capture performance is not affected by water and outperforms that of some commercial materials. Testing the performance of these MOFs in an industrial setting and consideration of the full capture process-including the targeted CO 2 sink, such as geological storage or serving as a carbon source for the chemical industry-will be necessary to identify the optimal separation material.
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It may be necessary to validate ML-interpolated and DFT-level predictions with experiment to ensure that predictions are realistic and materials are synthesizable.
“It may be necessary to validate the ML-interpolated and DFT-level predictions with experiment to ensure that predictions are realistic and materials are synthesizable, as was done for organic light-emitting diodes (50) or MOFs for wet flue gas capture (51).”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [51]
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- Authors
- Boyd, P. G., Chidambaram, A., García-Díez, E., Ireland, C. P., Daff, T. D., Bounds, R., Gładysiak, A., Schouwink, P., Moosavi, S. M., Maroto-Valer, M. M., Reimer, J. A., Navarro, J. A. R., Woo, T. K., Garcia, S., Stylianou, K. C., & Smit, B.
- Affiliations
- École Polytechnique Fédérale de Lausanne (CH)
- Heriot-Watt University (GB)
- University of Ottawa (CA)
- University of California, Berkeley (US)
- Lawrence Berkeley National Laboratory (US)
- Universidad de Granada (ES)
- Oregon State University (US)
- Corresponding author
- Woo, Tom K.
- Stylianou, Kyriakos C.
- Smit, Berend
- ORCID
- Venue
- Nature
- Published
- 2019-12-11
- Volume
- 576
- Issue
- 7786
- Pages
- 253-256
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2992843173
- Cited by
- 885
- References
- 29
- Citations by year
- 2026: 128 · 2025: 168 · 2024: 176 · 2023: 135 · 2022: 110 · 2021: 111 · 2020: 56 · 2019: 1
- FWCI
- 26.03
- Type
- journal article
- Language
- en
- Open access
- green
- www.osti.gov
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Cambridge University Engineering Department Publications Database (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Apollo (University of Cambridge) (submittedVersion)
- Apollo
- Funders
- National Science Foundation (DEAC02-05CH11231)
- U.S. Department of Energy (-AC02-05CH11231, 05CH11231, DE AC02 05CH11231, DESC0001015, DE-AC02-)
- Heriot-Watt University
- Compute Canada
- European Commission (760899, HORIZON2020, DE-AC02-05CH11231, 709741, 666983)
- European Synchrotron Radiation Facility
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (166888)
- Office of Fossil Energy (DEAC02-05CH11231)
- Office of Science (DEAC02–05CH11231, AC02-05CH11231, DE-AC02, DESC0001015)
- Natural Sciences and Engineering Research Council of Canada (DE-AC02-05CH11231)
- Engineering and Physical Sciences Research Council (DE-AC02-05CH11231, EP/N024540/1)
- Natural Environment Research Council (DE-AC02-05CH11231)
- Research Fund for Coal and Steel (709741)
- National Supercomputing Center, Korea Institute of Science and Technology Information
- Basic Energy Sciences (DE-AC02, DESC0001015, DE-AC02-05CH11231., AC02–05CH11231, SC0001015)
- H2020 European Research Council (666983)
- Topics
- Physical Sciences › Chemistry › Inorganic Chemistry › Metal-Organic Frameworks: Synthesis and Applications
- Physical Sciences › Engineering › Mechanical Engineering › Carbon Dioxide Capture Technologies
- Physical Sciences › Materials Science › Materials Chemistry › Covalent Organic Framework Applications
- Keywords
- metal-organic frameworks, CO2 capture, flue gas, wet flue gas, CO2/N2 selectivity, water stability, CO2 binding sites, data-driven materials design, carbon capture and storage, hydrophobic MOFs
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