High-Throughput Fluorescent Screening and Machine Learning for Feature Selection of Electrocatalysts for the Alkaline Hydrogen Oxidation Reaction

Hitt, Jeremy L.; Yoon, Dasol; Shallenberger, Jeffrey R.; Muller, David A.; Mallouk, Thomas E.

doi:10.1021/acssuschemeng.2c05170

High-Throughput Fluorescent Screening and Machine Learning for Feature Selection of Electrocatalysts for the Alkaline Hydrogen Oxidation Reaction

Journal Article · 23 November 2022 · ACS Sustainable Chemistry & Engineering

DOI:https://doi.org/10.1021/acssuschemeng.2c05170· OSTI ID:2421314

Hitt, Jeremy L. ^[1]; ^[2]; Shallenberger, Jeffrey R. ^[3]; Muller, David A. ^[4]; ^[1]

Department of Chemistry, The University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
Department of Materials Science and Engineering, Cornell University, Ithaca, New York 14853, United States
Materials Research Institute, Pennsylvania State University, University Park, Pennsylvania 16802, United States
School of Applied and Engineering Physics, Cornell University, Ithaca, New York 14853, United States; Kavli Institute at Cornell for Nanoscale Science, Ithaca, New York 14853, United States

Not provided.

View Accepted Manuscript (DOE)

Research Organization:: Cornell Univ., Ithaca, NY (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: SC0019445

OSTI ID:: 2421314

Journal Information:: ACS Sustainable Chemistry & Engineering, Journal Name: ACS Sustainable Chemistry & Engineering Journal Issue: 49 Vol. 10; ISSN 2168-0485

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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