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Rational design of high-loading electrocatalytic electrodes: from static multiscale integration to dynamic

Zijing Suo¹, Yuyao Sun¹, Jianping Lai¹

¹Key Laboratory of Eco-chemical Engineering, Key Laboratory of Optic-electric Sensing and Analytical Chemistry of Life Science, Ministry of Education, Taishan Scholar Advantage and Characteristic Discipline Team of Eco-chemical Process and Technology, College of Chemistry and Molecular Engineering, Qingdao University of Science and Technology, Qingdao 266042, P. R. China. jplai@qust.edu.cn.

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|June 17, 2026

Summary

This review shifts from static electrode design to dynamic intelligent systems for high-loading electrocatalysis. Integrating adaptive materials and smart interfaces enhances performance and stability in electrochemical energy devices.

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Area of Science:

Electrocatalysis
Materials Science
Chemical Engineering

Background:

Traditional high-loading electrodes fail under dynamic operating conditions due to static designs.
Performance degradation occurs at high current densities because of evolving electrode interfaces.
Existing static multiscale design approaches are insufficient for real-world electrocatalysis.

Purpose of the Study:

Propose a paradigm shift from static design to dynamic intelligent system integration for electrocatalysis.
Develop intelligent electrode systems with sensing, adapting, and self-optimizing capabilities.
Enhance catalytic activity, stability, and mass transfer under high-loading conditions.

Main Methods:

Analysis of dynamic failure mechanisms in high-loading electrodes across multiple scales.
Integration of dynamically reconstructable materials, bioinspired adaptive architectures, and smart interfaces.
Application of digital twin networks and machine learning for closed-loop design, diagnosis, and optimization.

Main Results:

Summarized advances in dynamic and intelligent regulation at atomic (self-healing), structural (bioinspired networks), interface (smart interfaces), and manufacturing (dry processing) scales.
Demonstrated the synergistic enhancement of catalytic activity, stability, and mass transfer.
Established digital twin and machine learning as enabling platforms for electrode system optimization.

Conclusions:

The dynamic intelligent system integration paradigm overcomes limitations of static electrode designs.
Next-generation electrochemical energy devices require self-sensing, self-optimizing, and long-lasting capabilities.
Future directions include dynamic evaluation systems, adaptive electrodes, and green intelligent manufacturing.