In situ Studies of Electrochemical Energy Conversion and Storage Technologies: From Materials, Intermediates, and Products to Surroundings

Escalating global energy demands and climate urgency necessitate advanced electrochemical energy conversion and storage technologies (EECSTs) like electrocatalysis and rechargeable batteries. Improving their performance relies on elucidating reaction mechanisms and structure-performance relationships via in situ studies. This review summarizes recent in situ studies of EECSTs through a variety of advanced characterization techniques aiming at mapping reaction pathways for the rational design of overall high-performance reaction systems. We outline the principles, capabilities, advantages, and limitations of various in situ techniques. Their applications in in situ studies of fuel cells, water/CO<sub>2</sub> electrolysis, and lithium batteries are highlighted with representative examples. These studies enable dynamic tracking of chemical and structural evolution of overall reaction systems, including materials, intermediates, products, and surroundings during operation, providing insights critical to rational system design. Future advancements will involve integrating multimodal in situ/operando approaches with artificial intelligence to enable real-time monitoring at practical scales. Such integration promises precise mechanistic insights and robust structure-performance correlations, ultimately accelerating the development of high-performance EECSTs aligned with sustainability and market requirements.

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Publication Details

Journal
Nano-Micro Letters
Published
2026-01-05
DOI
https://doi.org/10.1007/s40820-025-02014-6
Citations
1
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
16.08

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In situ Studies of Electrochemical Energy Conversion and Storage Technologies: From Materials, Intermediates, and Products to Surroundings

Dong Jin-Chao, Xing Chen, Jianfeng Li, Xiumei Lin et al.
1 citations
Nano-Micro Letters
Machine Learning in Materials Science
16.08
article

In situ Studies of Electrochemical Energy Conversion and Storage Technologies: From Materials, Intermediates, and Products to Surroundings

Dong Jin-Chao, Xing Chen, Jianfeng Li, Xiumei Lin, Yu-Lin Sun
article en
1 citations

Abstract

Escalating global energy demands and climate urgency necessitate advanced electrochemical energy conversion and storage technologies (EECSTs) like electrocatalysis and rechargeable batteries. Improving their performance relies on elucidating reaction mechanisms and structure-performance relationships via in situ studies. This review summarizes recent in situ studies of EECSTs through a variety of advanced characterization techniques aiming at mapping reaction pathways for the rational design of overall high-performance reaction systems. We outline the principles, capabilities, advantages, and limitations of various in situ techniques. Their applications in in situ studies of fuel cells, water/CO<sub>2</sub> electrolysis, and lithium batteries are highlighted with representative examples. These studies enable dynamic tracking of chemical and structural evolution of overall reaction systems, including materials, intermediates, products, and surroundings during operation, providing insights critical to rational system design. Future advancements will involve integrating multimodal in situ/operando approaches with artificial intelligence to enable real-time monitoring at practical scales. Such integration promises precise mechanistic insights and robust structure-performance correlations, ultimately accelerating the development of high-performance EECSTs aligned with sustainability and market requirements.

Nano-Micro LettersVol. 18(1)
Xiamen University (CN), Tan Kah Kee Innovation Laboratory (CN), Collaborative Innovation Center of Chemistry for Energy Materials (CN), Minnan Normal University (CN)
National Natural Science Foundation of China, China Postdoctoral Science Foundation, Dalian University of Technology, National Key Research and Development Program of China, Beijing National Laboratory for Molecular Sciences
Openalex Percentile: Top 4%
Machine Learning in Materials Science
16.08
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