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Reproducible Sorbent Materials Foundry for Carbon Capture at Scale



Austin McDannald, Howie Joress, Brian DeCost, Avery Baumann, A. Gilad Kusne, Kamal Choudhary, Taner N. Yildirim, Daniel Siderius, Winnie Wong-Ng, Andrew J. Allen, Christopher Stafford, Diana Ortiz-Montalvo


We envision an autonomous sorbent materials foundry (SMF) for rapidly evaluating materials for direct air capture of carbon dioxide ( CO2), specifically targeting novel metal organic framework materials. Our proposed SMF is hierarchical, simultaneously addressing the most critical gaps in the inter-related space of sorbent material synthesis, processing, properties, and performance. The ability to collect these critical data streams in an agile, coordinated, and automated fashion will enable efficient end-to-end sorbent materials design through machine learning driven research framework.


McDannald, A. , Joress, H. , DeCost, B. , Baumann, A. , Kusne, A. , Choudhary, K. , Yildirim, T. , Siderius, D. , Wong-Ng, W. , Allen, A. , Stafford, C. and Ortiz-Montalvo, D. (2022), Reproducible Sorbent Materials Foundry for Carbon Capture at Scale, Matter, [online],, (Accessed July 18, 2024)


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Created September 22, 2022, Updated November 29, 2022