Machine-learning-assisted material discovery of oxygen-rich highly porous carbon active materials for aqueous supercapacitors

Tao Wang, Runtong Pan, Murillo L. Martins, Jinlei Cui, Zhennan Huang, Bishnu P. Thapaliya, Chi-Linh Do-Thanh, Musen Zhou, Juntian Fan, Zhenzhen Yang, Miaofang Chi, Takeshi Kobayashi, Jianzhong Wu, Eugene Mamontov and Sheng Dai ()
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Tao Wang: Oak Ridge National Laboratory
Runtong Pan: University of California
Murillo L. Martins: Oak Ridge National Laboratory
Jinlei Cui: U.S. DOE Ames National Laboratory
Zhennan Huang: Center for Nanophase Materials Sciences, Oak Ridge National Laboratory
Bishnu P. Thapaliya: Oak Ridge National Laboratory
Chi-Linh Do-Thanh: University of Tennessee
Musen Zhou: University of California
Juntian Fan: University of Tennessee
Zhenzhen Yang: Oak Ridge National Laboratory
Miaofang Chi: Center for Nanophase Materials Sciences, Oak Ridge National Laboratory
Takeshi Kobayashi: U.S. DOE Ames National Laboratory
Jianzhong Wu: University of California
Eugene Mamontov: Oak Ridge National Laboratory
Sheng Dai: Oak Ridge National Laboratory

Nature Communications, 2023, vol. 14, issue 1, 1-13

Abstract: Abstract Porous carbons are the active materials of choice for supercapacitor applications because of their power capability, long-term cycle stability, and wide operating temperatures. However, the development of carbon active materials with improved physicochemical and electrochemical properties is generally carried out via time-consuming and cost-ineffective experimental processes. In this regard, machine-learning technology provides a data-driven approach to examine previously reported research works to find the critical features for developing ideal carbon materials for supercapacitors. Here, we report the design of a machine-learning-derived activation strategy that uses sodium amide and cross-linked polymer precursors to synthesize highly porous carbons (i.e., with specific surface areas > 4000 m2/g). Tuning the pore size and oxygen content of the carbonaceous materials, we report a highly porous carbon-base electrode with 0.7 mg/cm2 of electrode mass loading that exhibits a high specific capacitance of 610 F/g in 1 M H2SO4. This result approaches the specific capacitance of a porous carbon electrode predicted by the machine learning approach. We also investigate the charge storage mechanism and electrolyte transport properties via step potential electrochemical spectroscopy and quasielastic neutron scattering measurements.

Date: 2023
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DOI: 10.1038/s41467-023-40282-1

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