Institute of Metal Research, Chinese Academy of Science
Feng Li is a professor at Institute of Metal Research, Chinese Academy of Sciences (IMR, CAS). He received his Ph.D. in materials science at IMR, CAS in 2001 supervised by Prof. Hui-Ming Cheng. He mainly works on the energy materials. He has published more 350 papers on peer-reviewed journals, such as Nature Energy, Nano Energy, Energy Storage Materials, Adv Mater, J Energy Chem, etc. with more 66000 citations and H-index about 106. He obtained the award of National Science Fund for Distinguished Young Scholars by National Foundation of Science China, and Highly Cited Researcher by Clarivate Analytics from 2016 to 2023.
As a bridge between secondary batteries and electrochemical capacitors, hybrid charge storage devices are considered promising for simultaneously achieving high energy, power, and long cycling. However, limited by the difference in charge storage mechanisms and the strong coupling relationship between various properties, it is difficult to exert the advantages of cathode and anode in all cases. To this end, we proposed a transformative discussion on how to maximize the performance of hybrid charge storage devices from an electrochemical perspective. During the discussion, the zero-voltage potential was found to be the key to balance the capacity of the cathode and anode, and the self-matching of potential that we proposed and defined was the key that affects the dynamic electrochemical process. Combining electrochemical behaviour, testing methods, and electrode materials, we performed an exhaustive analysis of the coupling and matching of ideal hybrid charge storage in terms of capacity, kinetics and cycle life. In addition, considering the diversity of practical electrochemical systems, we also discussed the more complex electrochemical behaviours involved and the corresponding specific coupling and matching relationships. The differences in electrodes mean that coupling and matching are unavoidable for any charge storage system. More importantly, the corresponding theoretical research means that the electrochemical behaviour can be modelled and analysed by machine learning to predict the overall performance of the device and optimize the design based on the collected data. Our analysis, discussion and insights provide new an idea and direction for the research and development of hybrid charge storage and other systems.
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