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Updated: Sep 19, 2025

Author Spotlight: Tracking Electrochemistry on Single Nanoparticles with Surface-Enhanced Raman Scattering Spectroscopy and Microscopy
Published on: May 12, 2023
Beyond macroscopic performance: nanoscale charge transfer dynamics in energy storage/conversion devices via scanning
Zhenjiang Cao1, Pengfei Li1, Yujia He1
1School of Chemistry, Engineering Research Center of Energy Storage Materials and Devices, Ministry of Education, National Innovation Platform (Center) for Industry-Education Integration of Energy Storage Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Engineering Research Center of Energy Storage Material and Chemistry, Universities of Shaanxi Province, Xi'an Jiaotong University, Xi'an 710049, China. rui.gao@xjtu.edu.cn.
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The performance of electrochemical energy storage and conversion devices is fundamentally governed by nanoscale charge transfer dynamics at buried interfaces, which remain elusive to conventional macroscopic characterization techniques. Scanning electrochemical cell microscopy (SECCM) uniquely combines single-point probing with areal scanning to resolve localized electrochemical activity and bulk-scale architectural evolution, enabling cross-scale correlations between nanoscale charge transfer processes (<100 nm resolution) and macroscale electrode behavior (>100 μm). This capability establishes SECCM as a transformative tool for operando interrogation of interfacial phenomena, including metal ion deposition/insertion, stripping/extraction, and the distribution of active sites in electrocatalysts and the mechanism of degradation-induced failure, with millisecond temporal resolution. This review highlights recent advances in nanoscale charge transfer dynamics, focusing on energy storage material interfaces and electrochemical reaction mechanisms. Finally, we discuss emerging directions for SECCM, including operando visualization of multivalent metal dissolution/deposition (Li/Na/Zn anodes, Li-S conversion interfaces) and atomic-scale tracking of solid-state electrolyte degradation, particularly when integrated with machine learning, to accelerate the commercialization of next-generation sustainable energy technologies.
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