Reproducible Sorbent Materials Foundry for Carbon Capture at Scale

Published

September 22, 2022

Author(s)

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

Abstract

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.

Citation

Matter

Pub Type

Journals

Download Paper

https://doi.org/10.1016/j.xcrp.2022.101063

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Citation

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], https://doi.org/10.1016/j.xcrp.2022.101063, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=935142 (Accessed May 30, 2026)

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