A Review of Grain Boundaries: Formation Mechanism, Synthesis Strategy, and Application in Electrocatalysis
- 1Key Laboratory of Eco‐Chemical Engineering, Ministry of Education, International Science and Technology Cooperation Base of Eco‐Chemical Engineering and Green Manufacturing, College of Chemistry and Molecular Engineering Qingdao University of Science and Technology Qingdao China
- 2School of Materials Science and Engineering Liaocheng University Liaocheng China
- 3School of Chemistry and Chemical Engineering Qufu Normal University Qufu China
Abstract
Key electrocatalytic reactions such as HER, OER, ORR, CO2RR, and NRR offer promising routes for storing renewable energy as chemical fuels. However, their widespread application is constrained due to the lack of highly active and stable catalysts. Grain boundaries (GBs), with their undercoordinated sites, lattice strain, and excess free volume, present a promising strategy to overcome the activity–stability trade‐off. Yet, systematic reviews on GB engineering in electrocatalysis remain limited. This review provides a comprehensive framework, beginning with the fundamental formation mechanisms of GBs, integrating experimental and theoretical insights into the factors governing boundary evolution. Six synthesis methods—hydrothermal, electrodeposition, vapor phase, ball milling, molten salt, and laser ablation—are critically evaluated in terms of process parameters, resulting boundary characteristics, and practical trade‐offs. Their applications across five major electrocatalytic reactions are examined, correlating boundary features such as misorientation angle, type, and lattice strain with catalytic outcomes in activity, selectivity, and stability. We also explore the emerging role of machine learning in the rational design of GB architectures, including property prediction, phase discovery, and interpretable modeling. By integrating these aspects, this review establishes a unified framework for a rational design of high‐performance, boundary‐rich electrocatalysts for sustainable energy conversion.
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BibTeX
@article{gao2026review,
title = {{A Review of Grain Boundaries: Formation Mechanism, Synthesis Strategy, and Application in Electrocatalysis}},
author = {Jingyu Gao and Fengming Zhou and Xinying Li and Ying Deng and Zexing Wu and Siyuan Feng and Yujing Zhu and Dan Zhang and Zhenyu Xiao},
journal = {EcoEnergy},
year = {2026},
month = oct,
doi = {10.1002/ece2.70074},
url = {https://doi.org/10.1002/ece2.70074},
note = {CC BY},
}
RIS
TY - JOUR
TI - A Review of Grain Boundaries: Formation Mechanism, Synthesis Strategy, and Application in Electrocatalysis
AU - Jingyu Gao
AU - Fengming Zhou
AU - Xinying Li
AU - Ying Deng
AU - Zexing Wu
AU - Siyuan Feng
AU - Yujing Zhu
AU - Dan Zhang
AU - Zhenyu Xiao
PY - 2026
DA - 2026/10/24
JO - EcoEnergy
DO - 10.1002/ece2.70074
UR - https://doi.org/10.1002/ece2.70074
AB - Key electrocatalytic reactions such as HER, OER, ORR, CO2RR, and NRR offer promising routes for storing renewable energy as chemical fuels. However, their widespread application is constrained due to the lack of highly active and stable catalysts. Grain boundaries (GBs), with their undercoordinated sites, lattice strain, and excess free volume, present a promising strategy to overcome the activity–stability trade‐off. Yet, systematic reviews on GB engineering in electrocatalysis remain limited. This review provides a comprehensive framework, beginning with the fundamental formation mechanisms of GBs, integrating experimental and theoretical insights into the factors governing boundary evolution. Six synthesis methods—hydrothermal, electrodeposition, vapor phase, ball milling, molten salt, and laser ablation—are critically evaluated in terms of process parameters, resulting boundary characteristics, and practical trade‐offs. Their applications across five major electrocatalytic reactions are examined, correlating boundary features such as misorientation angle, type, and lattice strain with catalytic outcomes in activity, selectivity, and stability. We also explore the emerging role of machine learning in the rational design of GB architectures, including property prediction, phase discovery, and interpretable modeling. By integrating these aspects, this review establishes a unified framework for a rational design of high‐performance, boundary‐rich electrocatalysts for sustainable energy conversion.
SN - 2835-9399
SN - 2835-9380
ER -