杂原子
超级电容器
石墨烯
假电容
兴奋剂
材料科学
电容
纳米技术
电化学
电极
光电子学
化学
有机化学
物理化学
戒指(化学)
作者
Apiphu Chenwittayakhachorn,Kulpavee Jitapunkul,Bunyanuch Nakpalad,Phanit Worrayotkovit,Supawadee Namuangruk,Pichamon Sirisinudomkit,Pawin Iamprasertkun
出处
期刊:2D materials
[IOP Publishing]
日期:2023-01-18
卷期号:10 (2): 025003-025003
被引量:6
标识
DOI:10.1088/2053-1583/acaf8d
摘要
Abstract In recent years, graphene has been widely utilised as a supercapacitor electrode, and doping heteroatom on graphene is reported to enhance the pseudocapacitance of the electrode materials significantly resulting in a high energy density. However, the relationship and charge storage mechanism of a so-called ‘synergistic effect’ between those doped atoms including oxygen-, nitrogen-, and sulphur-doping on supercapacitor performances remain inscrutable. In this study, machine learning models are used to predict the capacitance of heteroatom-doped graphene-based supercapacitors and establish the effects of heteroatom-doping. Trained artificial neural network can accurately predict the capacitance of the electrode, drawing the best synthesis conditions for the heteroatom-doped graphene. Furthermore, we successfully demonstrate the synergistic effect that arises from co-doping nitrogen, sulphur, and locate the optimised region for N/S-co-doping with high capacitance, and high retention rate. Machine learning methods allow us to consider a much larger space of heteroatom-doping combinations to maximise the supercapacitor performances and provide a useful guideline for co-doping graphene-based supercapacitors.
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