Machine learning-guided discovery of gas evolving electrode bubble inactivation

Lake, Jack R.; Rufer, Simon; James, Jim; Pruyne, Nathan; Scourtas, Aristana; Schwarting, Marcus; Ambadkar, Aadit; Foster, Ian; Blaiszik, Ben; Varanasi, Kripa K.

doi:10.1039/D4NR02628D

Title: Machine learning-guided discovery of gas evolving electrode bubble inactivation

Journal Article · 15 January 2025 · Nanoscale

DOI: https://doi.org/10.1039/D4NR02628D · OSTI ID:2460568

Lake, Jack R. ^[1];

^[1]; James, Jim ^[2]; Pruyne, Nathan ^[2];

^[3]; Schwarting, Marcus ^[4]; Ambadkar, Aadit ^[2]; Foster, Ian ^[5];

^[3];

^[1]

Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA
Globus, University of Chicago, 5801 S Ellis Ave, Chicago, IL 60637, USA
Globus, University of Chicago, 5801 S Ellis Ave, Chicago, IL 60637, USA, Data Science and Learning Division, Argonne National Laboratory, 9700 S Cass Ave, Lemont, IL 60439, USA
Department of Computer Science, University of Chicago, 5801 S Ellis Ave, Chicago, IL 60637, USA
Data Science and Learning Division, Argonne National Laboratory, 9700 S Cass Ave, Lemont, IL 60439, USA, Department of Computer Science, University of Chicago, 5801 S Ellis Ave, Chicago, IL 60637, USA

Experimental interrogation unveils that as much as 75% of the area underneath bubbles is electrochemically active. A simple method for estimating the degree of electrode inactivation due to bubbles is demonstrated.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: AR0001220

OSTI ID:: 2460568

Journal Information:: Nanoscale, Journal Name: Nanoscale Journal Issue: 3 Vol. 17; ISSN NANOHL; ISSN 2040-3364

Publisher:: Royal Society of Chemistry (RSC)Copyright Statement

Country of Publication:: United Kingdom

Language:: English

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