超级电容器
水溶液
电极
碳纤维
材料科学
化学工程
纳米技术
化学
电化学
复合材料
有机化学
复合数
工程类
物理化学
作者
Runtong Pan,Mengyang Gu,Jianzhong Wu
标识
DOI:10.1021/acs.jced.4c00071
摘要
Doping carbon electrodes with heteroatoms such as nitrogen and oxygen proves effective in improving the performance of aqueous supercapacitors. However, the optimal conditions of N/O doping remain elusive due to the complexity of the porous structure and electrochemical behavior. While physics-based models face challenges in capturing the pseudocapacitance effects, direct empirical correlation of the capacitance with machine-learning (ML) methods may lead to erroneous predictions. In this work, we introduce a Gaussian process regression (GPR) method using a physical model as prior knowledge to limit the coupling effects of different input parameters. The physics-informed GPR proves effective in characterizing the capacitive behavior of N/O-codoped carbon electrodes in both 6 M KOH and 1 M H2SO4 aqueous solutions. Our machine-learning model suggests that the performance of aqueous supercapacitors can be maximized under acidic conditions by enhancing both the mesopore surface area and the O/N doping ratio of carbon electrodes.
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