Article(id=1319726238676136655, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319726164160139662, articleNumber=null, orderNo=null, doi=10.14062/j.issn.0454-5648.20240849, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1735574400000, receivedDateStr=2024-12-31, revisedDate=1737648000000, revisedDateStr=2025-01-24, acceptedDate=null, acceptedDateStr=null, onlineDate=1792576854627, onlineDateStr=2026-10-21, pubDate=1752940800000, pubDateStr=2025-07-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792576854627, onlineIssueDateStr=2026-10-21, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792576854627, creator=13701087609, updateTime=1792576854627, updator=13701087609, issue=Issue{id=1319726164160139662, tenantId=1146029695717560320, journalId=1263187385517883426, year='2025', volume='53', issue='7', pageStart='1777', pageEnd='2078', issueExtLink='null', onlineDate='null', pubDate='1752940800000', pubDateStr='2025-07-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792576836862, creator='13701087609', updateTime=1792577335628, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1319728256174117458, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319726164160139662, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1319728256174117459, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319726164160139662, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1938, endPage=1951, ext={EN=ArticleExt(id=1319726238890046160, articleId=1319726238676136655, tenantId=1146029695717560320, journalId=1263187385517883426, language=EN, title=Application and Progress of the Fixed Potential Method in Complex Interfacial Electrochemistry, columnId=1322569989907918962, journalTitle=Journal of the Chinese Ceramic Society, columnName=Special Issue on Computation and Design of Electrochemical Energy Storage Materials—Review, runingTitle=null, highlight=null, articleAbstract=
As energy sources diminish and ecological damage escalates, exploring green energy conversion and storage technologies becomes crucial. Electrocatalysis effectively converts green energy into chemical energy through electricity. In general, these reactions occur at double-layer interfaces, amidst a complex interface environment. It is essential for advancing electrochemistry research and the development of green energy technologies to clarify the interplay of ion-electron interactions, local potentials and electric fields. In the past decades, some researches on surface electrocatalysis have made significant progress since the standard hydrogen electrode model proposed by Nørskov et al. The model has effectively explained some origins of catalytic performance, and predicted some catalytic activity trends. However, the model has some limitations. The "electrocatalysts" encompass both "electricity" and "catalysts". In this model, ignoring the potential effect on the electronic properties of catalysts leads to the deviation from experimental results. In addition, the failure to consider the influence of the solution in theoretical calculations also becomes another factor affecting the reaction process.
This review represented the development and application on the Fixed Potential Method (FPM) based on first-principles calculations in complex interfacial electrochemistry. FPM emerges as a powerful tool for elucidating electrochemical reaction mechanisms, predicting material properties, and optimizing the design of electrochemical systems. The FPM significantly enhances the accuracy of simulating electrochemical reactions via precisely controlling the Fermi level of the reaction system and maintaining a constant electrode potential throughout simulations. Nowadays, FPM is extensively applied in the field of electrocatalysis to investigate the potential effects on the mechanisms of the oxygen evolution reaction (OER) and oxygen reduction reaction (ORR). Some work on transition metal-doped graphene (TM@graphene) catalysts reveal that the applied potential significantly affects the adsorption capacity of catalytic intermediates, charge transfer processes, and reaction pathways. The charge states of reaction intermediates exhibit a strong linear dependence on the applied potential, highlighting the importance of potential effects in catalytic mechanisms. In the realm of lithium-ion batteries, FPM is used to investigate the phase transformation processes of electrode materials. Some studies show that activation polarization induces a phase transition from the 2H phase to the 1T' phase in MoS2, which is crucial for understanding capacity fade in lithium-ion batteries. A research provides a theoretical framework for predicting phase transformations and discharge behavior via constructing an electrochemical phase diagram and fitting discharge curves based on zero-charge potential. In addition, FPM elucidates the concerted proton-electron transfer mechanism in proton migration within the electric double layer. Some studies demonstrate that proton migration from the outer Helmholtz layer to the electrode surface is accompanied by electron transfer, thus facilitating the migration process. The free energy changes during proton migration are affected by the applied potential and the local pH value gradient near the electrode surface, providing a theoretical basis for optimizing proton transport processes. These discoveries can enhance the comprehension of electrochemical interfacial reactions, and provide invaluable insights for the development of innovative catalysts and energy storage materials.
Summary and prospects FPM is proven to be an advanced computational method with broad application prospects in electrochemical research, such as exploring the potential effects in electrocatalytic water splitting, the activation polarization in the kinetics of lithium-ion batteries, and the concerted mechanism of proton-electron transfer within the electric double layer. These applications reveal the profound potential effects on the catalytic intermediate adsorption, charge transfer, and reaction pathways in both OER and ORR. Some researches on material phase transitions in lithium-ion batteries and their impact on battery performance also offer new avenues for improving cycle stability and energy density. Furthermore, studies on proton migration mechanisms provide theoretical guidance for optimizing proton transport at electrochemical interfaces, enhancing reaction selectivity, and boosting energy conversion efficiency. As computing power is enhanced and theoretical models are refined, the application of the FPM in electrochemical research becomes more widespread and in-depth. In addition, FPM enhances the accuracy and reliability of simulations, providing theoretical backing for the design of novel electrochemical materials, and shows promising potential for application in frontier fields like gate-controlled material phase transitions, magnetism, and electrosynthesis, offering innovative solutions to energy and environmental challenges with prospects.
However, FPM has its limitations. It can control the system to achieve fractional electron numbers, thereby realizing the grand canonical ensemble for electrons, but the complex interactions among multiple atoms are difficult to accurately describe. As a result, it cannot achieve non-integer changes in ion numbers to maintain a constant chemical potential, particularly for the systems with dynamic ion concentrations at chemical reaction interfaces. It is possible that machine learning potential fitting approaches based on first-principles calculations can offer a way to balance accuracy with the advantages of large-scale simulations. Nevertheless, the development of machine learning potentials for constant potential simulations is still in its early stage. Therefore, addressing large-scale and complex simulations under constant potential remains a challenge in future theoretical developments.
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电催化反应界面离子电子相互作用及局部电势电场环境复杂多变，恒电势模拟可以有效揭示界面反应过程。本文综述了固定电势法（FPM）在复杂界面电化学中的应用与进展，并阐述了FPM在探究电化学反应机理、预测材料性能、优化电化学系统设计等方面所展现出的独特优势。FPM通过精细调控反应体系的费米能级，使电极电位在模拟过程中保持恒定，显著提高了电化学反应过程的模拟精度。由于恒电势的引入，利用FPM成功揭示了电催化水分解的电势效应、锂离子电池中的活化极化在电极动力学过程中的核心作用，以及双电层中质子迁移的协同质子-电子转移机制。这些研究不仅加深了对电化学界面反应的理解，同时也为新型催化剂和电池材料的设计提供了理论指导。
, authors=薛育飞, 米婷婷, 张月飞, 张杨, 高国平, authorsList=薛育飞, 米婷婷, 张月飞, 张杨, 高国平, authorCompany=null, correspAuthors=高国平, authorNote=
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高国平（1988—），男，博士，副教授。
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XUE Yufei (1997-), male, Doctoral candidate. E-mail: xueyvfei@stu.xjtu.edu.cn
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XUE Yufei (1997-), male, Doctoral candidate. E-mail: xueyvfei@stu.xjtu.edu.cn
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薛育飞（1997—），男，博士研究生。
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薛育飞（1997—），男，博士研究生。
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