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  4. Deep Learning-assisted Tracking of Bubble Dynamics for Elucidating Oxygen Evolution Reaction on Nickel Electrodes
 
research article

Deep Learning-assisted Tracking of Bubble Dynamics for Elucidating Oxygen Evolution Reaction on Nickel Electrodes

Ashizawa, Daiki
•
Kurosu, Daiki
•
Itatani, Masaki
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February 15, 2026
Journal Of Electroanalytical Chemistry

The oxygen evolution reaction (OER) involves multiple proton-coupled electron transfer steps and complex reaction intermediates. The influence of interfacial gas evolution on its kinetic responses remains difficult to understand. In this study, nickel electrodes were employed as a model system to elucidate the relationship between O2 bubble dynamics and electrochemical behavior under electrochemical potential control. Video observation coupled with a deep learning analysis pipeline enabled precise recognition and temporal tracking of O2 bubbles during OER. Quantitative analysis of bubble growth enabled the extraction of current densities based on bubble growth. These findings highlight the critical role of interfacial gas dynamics in governing the kinetics of multi-electron transfer reactions and provide a new framework for mechanistic analysis of OER with respect to the conventional electrochemical measurements.

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Type
research article
DOI
10.1016/j.jelechem.2025.119760
Web of Science ID

WOS:001657903100001

Author(s)
Ashizawa, Daiki

Hokkaido University

Kurosu, Daiki

Hokkaido University

Itatani, Masaki

Hokkaido University

Oyamada, Nobuaki  

École Polytechnique Fédérale de Lausanne

Sato, Daiki

Hokkaido University

Kamada, Rui

Nagasaki University

Sakaguchi, Kazuyasu

Hokkaido University

Fukushima, Tomohiro

Hokkaido University

Murakoshi, Kei

Hokkaido University

Date Issued

2026-02-15

Publisher

ELSEVIER SCIENCE SA

Published in
Journal Of Electroanalytical Chemistry
Volume

1003

Article Number

119760

Subjects

Oxygen evolution reaction

•

Bubble

•

Ni

•

Electrochemistry

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LASPE  
FunderFunding(s)Grant NumberGrant URL

Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)

JP22H02023;JP22K18315;JP25H00862;JP25K22193

JST-Mirai Program

JPMJMI21EB

GteX Program

JPMJGX23H2

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Available on Infoscience
January 19, 2026
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/258166
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