Exploring the Electrocatalytic Oxygen Evolution/Urea Oxidation Activity of Solution-Combusted CuO/MnO2: Machine Learning Insights from OER Performance

Laya Agah; Arghavan Vojdani Saghir; Mostafa Nazemi; Sahar Mollazadeh Beidokhti; Jalil Vahdati-Khaki; Ghasem Barati Darband

doi:10.1039/d5ta09039c

January 1, 2026Journal of Materials Chemistry A

Exploring the Electrocatalytic Oxygen Evolution/Urea Oxidation Activity of Solution-Combusted CuO/MnO2: Machine Learning Insights from OER Performance

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Key Points

This research aims to explore the electrocatalytic performance of CuO/MnO2 for oxygen evolution and urea oxidation using machine learning methods.
Utilized solution-combusted CuO/MnO2 as the electrocatalyst.
Applied machine learning to analyze electrochemical performance metrics.
Conducted experiments to assess oxygen evolution reaction (OER) and urea oxidation rates.
CuO/MnO2 demonstrated significantly enhanced OER performance compared to other catalysts.
Machine learning revealed key performance indicators that predict electrocatalytic efficiency.
Findings suggest that CuO/MnO2 could be a viable alternative for sustainable energy applications.

Implication

Explores electrocatalytic activity of CuO/MnO2 for oxygen evolution, suggesting its potential in energy solutions.

Abstract

Driven by strategic and environmental imperatives, replacing fossil fuels with hydrogen from water splitting as a suitable and promising substitute energy carrier is essential. The electrochemical oxygen evolution reaction (OER)...

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Cite This Study

Agah et al. (2026) studied this question.

synapsesocial.com/papers/6a0d4ee2f03e14405aa9a16e https://doi.org/10.1039/d5ta09039c

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Exploring the Electrocatalytic Oxygen Evolution/Urea Oxidation Activity of Solution-Combusted CuO/MnO2: Machine Learning Insights from OER Performance

Key Points

Implication

Abstract

Discussion

Cite This Study

Exploring the Electrocatalytic Oxygen Evolution/Urea Oxidation Activity of Solution-Combusted CuO/MnO2: Machine Learning Insights from OER Performance

Key Points

Implication

Abstract

Discussion

Cite This Study

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