Machine learning-guided discovery of gas evolving electrode bubble inactivation
Name
d4nr02628d.pdf
Description
Published version
Size
1.85 MB
Format
Adobe PDF
Checksum (MD5)
1ff76760d583a7cb5a8037233f0701ba
Author(s)
Lake, Jack R
Rufer, Simon
James, Jim
Pruyne, Nathan
Scourtas, Aristana
Schwarting, Marcus
Ambadkar, Aadit
Foster, Ian
Blaiszik, Ben
Varanasi, Kripa K
Date Issued
October 8, 2024
Journal
Nanoscale
Publisher
Royal Society of Chemistry
Citation
Lake, Jack R, Rufer, Simon, James, Jim, Pruyne, Nathan, Scourtas, Aristana et al. 2024. "Machine learning-guided discovery of gas evolving electrode bubble inactivation." Nanoscale.
Version
Final published version
Abstract
The adverse effects of electrochemical bubbles on the performance of gas-evolving electrodes are well known, but studies on the degree of adhered bubble-caused inactivation, and how inactivation changes during bubble evolution are limited. We study electrode inactivation caused by oxygen evolution while using surface engineering to control bubble formation. We find that the inactivation of the entire projected area, as is currently believed, is a poor approximation which leads to non-physical results. Using a machine learning-based image-based bubble detection method to analyze large quantities of experimental data, we show that bubble impacts are small for surface engineered electrodes which promote high bubble projected areas while maintaining low direct bubble contact. We thus propose a simple methodology for more accurately estimating the true extent of bubble inactivation, which is closer to the area which is directly in contact with the bubbles.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1039/d4nr02628d
