Journal
MICROPOROUS AND MESOPOROUS MATERIALS
Volume 314, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.micromeso.2020.110870
Keywords
Machine learning; Pore sizes; Mesoporous carbon; Mesoporous metal compounds
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Funding
- National Natural Science Foundation of China [51671136]
- Startup Research Fund of Dongguan University of Technology [KCYKYQD2017015, KCYCXPT2017005]
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This article focuses on the relationship between pore size and capacitance in supercapacitor electrode materials, discusses the application of machine learning in this aspect, summarizes the synthesis methods of mesoporous carbon, metal oxides, etc. as capacitor materials, and emphasizes the important role of mesopores in materials.
Supercapacitor has received considerable attention as a promising energy storage device due to its fast charging, excellent cyclic stability, and high-power density. In order to fully realize its potential to bridge the energy storage devices, there is a strong need to have a deeper understanding in the relationship between capacitance and pore sizes of the electrode materials, in particular mesopores. As a result of its high surface area and abundant accessible active site, ease of mass/charge transfer, mesopomus material has triggered immense attention as potential material for energy-related applications. Herein, special attention will be focused on the machine learning application to better understand the pore size of the electrode material and electrolyte ion in nanopores. Furthermore, the fundamental understanding of the relationship between the pore sizes and capacitance is also discussed. We also summarize the current state-of-the-art synthetic methods of mesopomus carbon, metal oxide, metal phosphide, and metal sulfides as supercapacitor electrode materials. Some pivotal scientific challenges and perspectives on the understanding of the role of mesopores in the material are highlighted and present, respectively.
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