Nickel cobalt phosphate/phosphide as a promising electrode material for extrinsic supercapacitors: machine learning analysis

被引:0
|
作者
Chavare, C. D. [1 ,3 ,4 ]
Sawant, D. S. [1 ]
Gaikwad, S. V. [1 ]
Fulari, A. V. [5 ]
Kulkarni, H. R. [4 ]
Dubal, D. P. [2 ]
Lohar, G. M. [1 ,2 ]
机构
[1] Lal Bahadur Shastri Coll Arts Sci & Commerce, Dept Phys, Satara 415002, Maharashtra, India
[2] Queensland Univ Technol, Sch Chem & Phys, 2 George St, Brisbane, Qld 4000, Australia
[3] Annasaheb Magar Mahavidyalaya, Dept Phys, Pune 411028, Maharashtra, India
[4] G H Raisoni Coll Arts Commerce & Sci, Pune 412207, Maharashtra, India
[5] Symbiosis Int, Symbiosis Ctr Nanosci & Nanotechnol, Pune, India
关键词
HYDROGEN EVOLUTION REACTION; HIGH-PERFORMANCE ELECTRODE; REDUCED GRAPHENE OXIDE; PHOSPHATE/GRAPHENE FOAM COMPOSITE; ENERGY-STORAGE; ELECTROCHEMICAL PERFORMANCE; CONTROLLABLE FABRICATION; SOLVOTHERMAL SYNTHESIS; HYDROTHERMAL SYNTHESIS; MICRO-SUPERCAPACITORS;
D O I
10.1039/d4ta07613c
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Recently, transition metal compounds containing phosphorous, such as metal phosphates and phosphides, have attracted great attention for fabrication of energy storage devices such as supercapacitors. Benefiting from their high ion conductivity, good chemical stability, and metalloid properties, metal phosphates and phosphides show promise to deliver excellent charge storage capacity. The present review provides a comprehensive summary of recent advancements in nickel cobalt phosphates and phosphides, including their charge storage mechanisms, structural information using different analytical tools and morphology (1D, 2D, and 3D)-dependent electrochemical performance. The electronic structures of nickel cobalt phosphate/phosphide are intentionally reviewed using density functional theory results. Furthermore, for the first time, we introduce machine learning analysis as a tool to explore different parameters and predict supercapacitor behaviour with respect to different experimental and electrochemical parameters. Machine learning technology enhances accuracy, saves time, and efficiently analyzes energy storage materials. Finally, the challenges and future perspectives to enhance the supercapacitor performance of nickel cobalt phosphates and phosphides are discussed.
引用
收藏
页码:6993 / 7054
页数:62
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