Optimization of single crystal surface and interface structures for electrocatalysis

For emerging renewable and sustainable energy technologies, single crystal materials have become key materials to enhance electrocatalytic performance because of their atomic-level ordered structures and tailorable surface and interfacial properties. Various single crystal types, including metals, semiconductors, ceramics, organics, and nanocrystals, exhibit superior catalytic selectivity and stability in reactions such as water splitting and carbon/nitrogen cycles, benefiting from high electrical conductivity, tunable energy bands, and active sites with high surface energy. Through surface modification, interfacial atomic doping, and heterostructure construction, the distribution of active sites, electronic structure, and mass transport can be precisely regulated, significantly optimizing the catalytic kinetics of single crystal materials. In situ characterizations elucidate catalytic mechanisms at the atomic scale, while emerging methods like AI-assisted synthesis and bio-template directed growth offer pathways to overcome bottlenecks in the precision and cost of single crystal preparation. In addressing stability challenges in complex environments, strategies such as organic-inorganic hybridization and gradient interface design effectively mitigate interfacial instability. Future research should focus on cross-scale structural regulation and multidisciplinary integration to facilitate the transition of single crystal electrocatalysts from fundamental research to industrial applications, enabling efficient energy conversion.

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Publication Details

Journal
Materials Reports Energy
Published
2025-08-01
DOI
https://doi.org/10.1016/j.matre.2025.100358
Citations
4
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
2.07
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article

Optimization of single crystal surface and interface structures for electrocatalysis

Weijia Zhou, Bin Chang, Moyu Yi, Yongzhong Wu et al.
4 citations
Materials Reports Energy
Machine Learning in Materials Science
2.07
article

Optimization of single crystal surface and interface structures for electrocatalysis

Weijia Zhou, Bin Chang, Moyu Yi, Yongzhong Wu, Haiyan Liang, Haixiao Hu, Hehe Jiang, Xiaopeng Hao, Xiaoyan Liu
article en
4 citations

Abstract

For emerging renewable and sustainable energy technologies, single crystal materials have become key materials to enhance electrocatalytic performance because of their atomic-level ordered structures and tailorable surface and interfacial properties. Various single crystal types, including metals, semiconductors, ceramics, organics, and nanocrystals, exhibit superior catalytic selectivity and stability in reactions such as water splitting and carbon/nitrogen cycles, benefiting from high electrical conductivity, tunable energy bands, and active sites with high surface energy. Through surface modification, interfacial atomic doping, and heterostructure construction, the distribution of active sites, electronic structure, and mass transport can be precisely regulated, significantly optimizing the catalytic kinetics of single crystal materials. In situ characterizations elucidate catalytic mechanisms at the atomic scale, while emerging methods like AI-assisted synthesis and bio-template directed growth offer pathways to overcome bottlenecks in the precision and cost of single crystal preparation. In addressing stability challenges in complex environments, strategies such as organic-inorganic hybridization and gradient interface design effectively mitigate interfacial instability. Future research should focus on cross-scale structural regulation and multidisciplinary integration to facilitate the transition of single crystal electrocatalysts from fundamental research to industrial applications, enabling efficient energy conversion.

Materials Reports EnergyVol. 5(3)
Shandong University of Traditional Chinese Medicine (CN), Qilu University of Technology (CN), University of Jinan (CN), Shandong First Medical University (CN), King Abdullah University of Science and Technology (SA)
Openalex Percentile: Top 13%
Machine Learning in Materials Science
2.07
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