Machine-learning-assisted material discovery of oxygen-rich highly porous carbon active materials for aqueous supercapacitors
Abstract
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.
- Authors:
-
[1];
[2];
[1];
[1];
[5];
[2];
[1];
[4];
[2];
[6];
[1]
- 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)
- Publication Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Ames Laboratory (AMES), Ames, IA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- OSTI Identifier:
- 1994652
- Alternate Identifier(s):
- OSTI ID: 1993985
- Report Number(s):
- IS-J-11,122
Journal ID: ISSN 2041-1723
- Grant/Contract Number:
- AC05-00OR22725; AC02-07CH11358
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Nature Communications
- Additional Journal Information:
- Journal Volume: 14; Journal Issue: 1; Journal ID: ISSN 2041-1723
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Citation Formats
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, and Dai, Sheng. Machine-learning-assisted material discovery of oxygen-rich highly porous carbon active materials for aqueous supercapacitors. United States: N. p., 2023.
Web. doi:10.1038/s41467-023-40282-1.
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. Machine-learning-assisted material discovery of oxygen-rich highly porous carbon active materials for aqueous supercapacitors. United States. https://doi.org/10.1038/s41467-023-40282-1
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, and Dai, Sheng. Tue .
"Machine-learning-assisted material discovery of oxygen-rich highly porous carbon active materials for aqueous supercapacitors". United States. https://doi.org/10.1038/s41467-023-40282-1. https://www.osti.gov/servlets/purl/1994652.
@article{osti_1994652,
title = {Machine-learning-assisted material discovery of oxygen-rich highly porous carbon active materials for aqueous supercapacitors},
author = {Wang, Tao and Pan, Runtong and Martins, Murillo L. and Cui, Jinlei and Huang, Zhennan and Thapaliya, Bishnu Prasad and Do-Thanh, Chi-Linh and Zhou, Musen and Fan, Juntian and Yang, Zhenzhen and Chi, Miaofang and Kobayashi, Takeshi and Wu, Jianzhong and Mamontov, Eugene and Dai, Sheng},
abstractNote = {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.},
doi = {10.1038/s41467-023-40282-1},
journal = {Nature Communications},
number = 1,
volume = 14,
place = {United States},
year = {Tue Aug 01 00:00:00 EDT 2023},
month = {Tue Aug 01 00:00:00 EDT 2023}
}
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