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Probing the Oxygen Evolution Reaction Intermediates of Nickel Electrodes: Insights from Raman Mapping and Machine Learning in Alkaline Environments (Supporting Information)

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posted on 2025-07-29, 00:10 authored by Tomohiro FUKUSHIMA, Kenko TSUCHIMOTO, Masaki ITATANI, Kei MURAKOSHI
<p>Water electrolysis in alkaline conditions is one of the major processes for green hydrogen production. The overpotential of the oxygen evolution reaction (OER) is generally higher than that of the hydrogen evolution reaction (HER). Therefore, understanding the origin of the OER mechanism in alkaline conditions is important. Here, we utilized Raman spectroscopic analysis and machine learning techniques to study the OER from a Ni electrode to probe the OER intermediates in alkaline conditions. Raman spectroscopy was conducted using objective lenses with long working distance to avoid the damage of lens by alkaline electrolytes. This developed method can be used for the observation of the OER intermediates of Ni even in strong alkaline electrolytes. Raman mapping analysis revealed the variation of OER intermediates, which can be additional proof of the hidden reaction pathway from machine learning analysis. This study can be utilized as a methodology for the understanding of the multi-electron transfer reaction from the electrochemical and spectrochemical analyses.</p>

Funding

Control of Proton Conductivity by Vibrational Level Modulation

Japan Society for the Promotion of Science

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Electrochemistry of Polaritonic Substrates

Japan Society for the Promotion of Science

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Innovation in electrochemical energy conversion theory using strong coupling

Japan Science and Technology Agency

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Development of innovative water electrolysis system for green hydrogen production

Japan Science and Technology Agency

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Ministry of Education, Culture, Sports, Science and Technology

Creation of innovative water electrolysis process with hierarchical structure regulated catalyst electrode

Japan Science and Technology Agency

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Corresponding author email address

tfuku@sci.hokudai.ac.jp

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© 2025 The Author(s).

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