Citation: Zonglin Li, Shihua Zou, Zining Wang, Georgeta Postole, Liang Hu, Hongying Zhao. Machine learning in electrochemical oxidation process: A mini-review[J]. Chinese Chemical Letters, ;2025, 36(8): 110526. doi: 10.1016/j.cclet.2024.110526 shu

Machine learning in electrochemical oxidation process: A mini-review

Zonglin Li ^a ,
Shihua Zou ^a ,
Zining Wang ^a ,
Georgeta Postole ^b ,
Liang Hu ^{c, d} ,
Hongying Zhao ^{a, *,}

a.
School of Chemical Science and Engineering, and Shanghai Key lab of Chemical Assessment and Sustainability, Tongji University, Shanghai 200092, China
b.
Univ Lyon, Université Claude Bernard Lyon 1, CNRS, IRCELYON, F-69626, Villeurbanne, France
c.
College of Computer Science and Technology, Tongji University, Shanghai 201804, China
d.
DeepBlue Academy of Sciences, Shanghai 200336, China

hyzhao@tongji.edu.cn

Received Date: 11 May 2024
Revised Date: 23 August 2024
Accepted Date: 3 October 2024
Available Online: 5 October 2024

Figures(6)

In recent years, machine learning (ML) techniques have demonstrated a strong ability to solve highly complex and non-linear problems by analyzing large datasets and learning their intrinsic patterns and relationships. Particularly in chemical engineering and materials science, ML can be used to discover microstructural composition, optimize chemical processes, and create novel synthetic pathways. Electrochemical processes offer the advantages of precise process control, environmental friendliness, high energy conversion efficiency and low cost. This review article provides the first systematic summary of ML in the application of electrochemical oxidation, including pollutant removal, battery remediation, substance synthesis and material characterization prediction. Hot trends at the intersection of ML and electrochemical oxidation were analyzed through bibliometrics. Common ML models were outlined. The role of ML in improving removal efficiency, optimizing experimental conditions, aiding battery diagnosis and predictive maintenance, and revealing material characterization was highlighted. In addition, current issues and future perspectives were presented in relation to the strengths and weaknesses of ML algorithms applied to electrochemical oxidation. In order to further support the sustainable growth of electrochemistry from basic research to useful applications, this review attempts to make it easier to integrate ML into electrochemical oxidation.
- Machine learning,
- Electrochemical oxidation,
- Pollutant removal,
- Lithium-ion battery,
- Material synthesis
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