硅酸盐学报

基于活性基元序构调控的负载金属型CO₂电还原催化剂理性设计研究进展

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邱成龙 , 王家成

硅酸盐学报 | 综合评述 2026,54(7): 2249-2259

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硅酸盐学报 |综合评述 2026 , 54 (7) : 2249 -2259

基于活性基元序构调控的负载金属型CO₂电还原催化剂理性设计研究进展

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邱成龙, 王家成

作者信息

台州学院，全省海岛绿色能源及新材料重点实验室，浙江台州 318000

通讯作者:

王家成（1979—），男，博士，教授。

作者简介:

邱成龙（1995—），男，博士，讲师。

Recent Progess on Rational Design of Supported Metal CO₂ Electroreduction Catalysts Based on Structural Ordering Regulation of Active Functional Units

Chenglong QIU, Jiacheng WANG

Affiliations

Zhejiang Key Laboratory of Island Green Energy and New Materials, Taizhou University, Taizhou 318000, Zhejiang, China

Corresponding Author:

{WANG} Jiacheng (1979-), male, Ph.D., Professor. E-mail: jiacheng.wang@tzc.edu.cn

QIU Chenglong (1995-), male, Ph.D., Lecturer. E-mail: qiuchenglong@tzc.edu.cn

出版时间: 2026-06-03 doi: 10.14062/j.issn.0454-5648.20260117

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摘要

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电催化CO₂还原反应（CO₂RR）涉及多电子、多质子耦合过程，反应路径复杂、产物分布多样，其催化性能高度依赖于活性位点对关键中间体的吸附与转化能力。负载金属催化剂是CO₂RR体系中常用的一类催化材料，具有单原子位点、双原子位点、纳米颗粒等多样化的活性功能基元。这些功能基元的序构特征决定了反应物吸附行为、反应路径及能垒分布，是影响催化活性、选择性与稳定性的核心因素。密度泛函理论、分子动力学等计算方法为预测活性位点结构、揭示催化反应本质提供了重要手段。然而，面对催化剂表面结构复杂、构型空间庞大的挑战，机器学习方法的引入为活性功能基元的高通量筛选与理性设计开辟了新的研究范式。基于此，本文从功能基元序构视角出发，系统综述了负载金属催化剂中典型活性位点类型及其构效关系，重点讨论了几何构型与电子结构对CO₂RR性能的调控机制，并总结了机器学习在活性功能基元预测与设计中的应用进展。最后，针对CO₂RR活性功能基元精准设计面临的挑战与发展趋势进行了展望。

关键词

电催化二氧化碳还原 / 负载金属催化剂 / 活性功能基元 / 构效关系 / 理性设计

Abstract

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Electrocatalytic CO₂ reduction reaction (CO₂RR) is widely recognized as a promising strategy for converting greenhouse gas CO₂ into value-added chemicals and fuels, thereby contributing to carbon neutrality and renewable energy storage. However, CO₂RR involves complex multi-electron and multi-proton transfer processes, leading to diverse reaction pathways and product distributions. The catalytic performance is fundamentally governed by the adsorption and transformation of key intermediates (e.g., *COOH, *CO,*OCHO), which are highly sensitive to the local structure and electronic properties of active sites.

Supported metal catalysts emerge as an important class of CO₂RR catalysts due to their structural tunability and versatile catalytic behavior. Recent studies reveal that catalytic reactions predominantly occur at localized active centers rather than the entire catalyst surface. These active centers can be described as "functional units" with specific geometric and electronic characteristics, including single-atom sites, dual-atom sites, and nanoparticle sites. The spatial arrangement and interaction of these functional units form a "structural ordering" feature, which plays a decisive role in determining adsorption behavior, reaction pathways, and energy barriers. Understanding the structure-performance relationship from the perspective of functional unit ordering is thus essential for rational catalyst design.

This review summarizes recent progress on supported metal catalysts for CO₂RR from the viewpoint of functional unit structural ordering. First, the structural characteristics and catalytic mechanisms of typical functional units are discussed. Single-atom sites exhibit highly tunable electronic structures through coordination engineering, enabling a precise control over intermediate adsorption and offering opportunities to break conventional scaling relationships. Dual-atom sites introduce metal-metal electronic coupling and spatial synergy, which can optimize intermediate binding and promote complex reactions such as C-C coupling. Nanoparticle sites, characterized by continuous surfaces and multiple active centers, provide cooperative platforms where size effects, facet engineering, and metal-support interactions collectively affect catalytic performance. In these systems, the synergy between geometric configuration and electronic structure governs the activity, selectivity, and stability of CO₂RR.

Despite significant progress, a rational catalyst design remains challenging due to the vast configurational space and complex coupling between geometric and electronic effects. To address these challenges, machine learning (ML) is increasingly integrated into catalyst research. This review further highlights recent advances on ML-assisted design of functional units. Descriptor-based ML models establish quantitative relationships between structural features and catalytic performance, enabling efficient high-throughput screening of candidate catalysts. In addition, machine learning potentials (MLPs) also provide near density functional theory (DFT) accuracy with a significantly reduced computational cost. These methods enable large-scale structure exploration and dynamic simulations, offering insights into the evolution of active sites under realistic reaction conditions.

Finally, some challenges and future perspectives are discussed. Key issues include the lack of realistic dynamic models for active sites under operating conditions, the absence of unified multiscale frameworks linking atomic-scale properties to macroscopic performance, and the limited transferability of ML models due to descriptor and data constraints. Future research should focus on developing multiscale theoretical-experimental approaches, constructing physically interpretable descriptor systems, and integrating ML with high-quality databases and advanced characterization techniques. Such efforts are expected to enable a precise regulation of functional unit ordering and accelerate the transition from empirical optimization to predictive catalyst design.

Overall, the paradigm shift from "global structure optimization" to "precise control of functional unit ordering," combined with the integration of theory, machine learning, and advanced characterization, provides a promising pathway for the rational design of high-performance CO₂RR catalysts and the advancement of sustainable energy technologies.

Summary and Prospects

This review systematically summarizes the structure-performance relationships of supported metal catalysts for CO₂RR from the perspective of structural ordering of active functional units. It is emphasized that catalytic reactions are governed by localized active centers rather than the overall catalyst structure. The spatial arrangement, coordination environment, and electronic properties of single-atom, dual-atom, and nanoparticle sites collectively determine the adsorption and transformation of key intermediates (e.g., *COOH and *CO), thereby regulating activity, selectivity, and stability. In different functional units, a unified understanding emerges. The catalytic performance is dictated by the synergistic coupling between geometric configuration and electronic structure. Single-atom sites enable a precise electronic regulation through coordination engineering, dual-atom sites introduce metal-metal coupling and cooperative effects that can break scaling relationships, and nanoparticle sites provide multi-site platforms governed by size, facet, and interface effects. These multilevel interactions construct a complex yet systematic structure-performance network. Furthermore, the paradigm of catalyst design shifts from empirical trial-and-error approaches to data-driven strategies. Machine learning methods, including descriptor-based models and machine learning potentials, significantly improve the efficiency of catalyst screening and enable the exploration of large configurational spaces and dynamic structural evolution. The integration of theoretical calculations, machine learning, and experimental studies accelerates the transition toward rational catalyst design.

Despite substantial progress, several key challenges remain in the rational design of CO₂RR catalysts. First, active sites often undergo dynamic reconstruction under realistic electrochemical conditions, while most of the existing studies rely on static models. Developing simulation methods that incorporate potential, solvent, and interface effects, together with advanced in-situ characterization techniques, is essential for identifying true active structures. Second, the lack of a unified multiscale framework limits the understanding of how atomic-scale electronic structures translate into macroscopic catalytic performance. Bridging this gap requires establishing quantitative relationships across multiple scales, from electronic structure and local geometry to interfacial properties and mass transport processes. Finally, a predictive capability of machine learning models is still constrained by descriptor selection and data quality. Developing physically interpretable descriptors and constructing high-quality datasets, combined with strategies such as active learning and transfer learning, will be crucial to improving model generalization and applicability.

Overall, the transition from "experimental observation with theoretical interpretation" to "theory-guided prediction with experimental validation," as well as from "global structural optimization" to the "precise regulation of structural ordering of functional units," represents a key trend in the development of supported metal catalysts. In the future, a precise regulation of complex multi-electron reaction pathways is expected to be achieved via the synergistic design of geometric and electronic structures and the deep integration of theoretical calculations, machine learning, and advanced characterization techniques, providing a more robust theoretical and material foundation for CO₂ utilization and sustainable energy conversion.

Key words

electrocatalytic carbon dioxide reduction / supported metal catalysts / active functional units / structure-performance relationships / rational design

引用本文

邱成龙, 王家成. 基于活性基元序构调控的负载金属型CO₂电还原催化剂理性设计研究进展. 硅酸盐学报, 2026 , 54 (7) : 2249 -2259 . DOI: 10.14062/j.issn.0454-5648.20260117

Chenglong QIU, Jiacheng WANG. Recent Progess on Rational Design of Supported Metal CO₂ Electroreduction Catalysts Based on Structural Ordering Regulation of Active Functional Units[J]. Journal of the Chinese Ceramic Society, 2026 , 54 (7) : 2249 -2259 . DOI: 10.14062/j.issn.0454-5648.20260117

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国家自然科学基金(22508277; 52472231; 52311530113; W2521017)

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2026年第54卷第7期

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文章信息

doi: 10.14062/j.issn.0454-5648.20260117

接收时间：2026-03-03
首发时间：2026-10-22
出版时间：2026-06-03

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收稿日期：2026-03-03
修回日期：2026-04-06

基金

国家自然科学基金(22508277; 52472231; 52311530113; W2521017)

作者信息

台州学院，全省海岛绿色能源及新材料重点实验室，浙江台州 318000

通讯作者:

王家成（1979—），男，博士，教授。

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https://castjournals.cast.org.cn/joweb/gsyxb/CN/10.14062/j.issn.0454-5648.20260117

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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