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

Wang, Tao; Pan, Runtong; Martins, Murillo L.; Cui, Jinlei; Huang, Zhennan; Thapaliya, Bishnu Prasad; Do-Thanh, Chi-Linh; Zhou, Musen; Fan, Juntian; Yang, Zhenzhen; Chi, Miaofang; Kobayashi, Takeshi; Wu, Jianzhong; Mamontov, Eugene; Dai, Sheng

doi:10.1038/s41467-023-40282-1

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

Journal Article · Tue Aug 01 00:00:00 EDT 2023 · Nature Communications

DOI:https://doi.org/10.1038/s41467-023-40282-1· OSTI ID:1994652

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Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States). Institute for Advanced Materials and Manufacturing
Univ. of California, Riverside, CA (United States)
Ames Laboratory (AMES), Ames, IA (United States)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS)
Univ. of Tennessee, Knoxville, TN (United States). Institute for Advanced Materials and Manufacturing
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

AbstractPorous 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.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Ames Laboratory (AMES), Ames, IA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: AC05-00OR22725; AC02-07CH11358

OSTI ID:: 1994652

Alternate ID(s):: OSTI ID: 1993985

Report Number(s):: IS-J-11,122

Journal Information:: Nature Communications, Vol. 14, Issue 1; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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