新しい炭素材料がエネルギー蓄積記録を更新、スーパーキャパシタの進歩につながる可能性が高い(New carbon material sets energy-storage record likely to advance supercapacitors)

2023-11-21 オークリッジ国立研究所(ORNL)

＜関連情報＞

• https://www.ornl.gov/news/new-carbon-material-sets-energy-storage-record-likely-advance-supercapacitors
• https://www.nature.com/articles/s41467-023-40282-1

機械学習支援による水性スーパーキャパシタ用酸素リッチ高多孔性炭素活物質の材料発見 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 &Sheng Dai
Nature Communications  Published:01 August 2023
DOI:https://doi.org/10.1038/s41467-023-40282-1

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.