A review on copper-based chalcogenide materials for supercapacitor application: exploring through experimental evidence and machine learning

Abstract

To unlock the full potential of supercapacitors, it is essential to explore novel materials with tuneable electrochemical properties. Transition metal chalcogenides, in particular copper chalcogenides, have shown immense potential to achieve a next-generation electrode material. This review aims to explore copper-based chalcogenides as promising candidates, highlighting their rich redox activity, high intrinsic conductivity, and structural tunability. We also discuss how variations in morphology, doping effects, and the formation of composites significantly influence electrochemical performance. Along with that the hybridisation of other metallic elements into binary copper chalcogenides, which significantly enhanced conductivity, stability, and redox activity, is addressed. Furthermore, we briefly address few engineering strategies used to amplify the electrochemical performance of copper chalcogenide-based supercapacitors. This review also evaluates the practical applicability of the chalcogenides in a real-world scenario based on the current literature. In addition, it briefly discusses the emerging use of machine learning approaches to predict the electrochemical performance of copper chalcogenide-based systems. Finally, the key challenges associated with scalability, long-term cycling stability, and environmental impact are examined, alongside perspectives for future research directions aimed at overcoming these limitations.

Graphical abstract: A review on copper-based chalcogenide materials for supercapacitor application: exploring through experimental evidence and machine learning

Article information

Article type
Review Article
Submitted
10 Jun 2025
Accepted
15 Sep 2025
First published
16 Sep 2025
This article is Open Access
Creative Commons BY-NC license

J. Mater. Chem. A, 2025, Advance Article

A review on copper-based chalcogenide materials for supercapacitor application: exploring through experimental evidence and machine learning

M. Mohan Ram, R. Sapna, S. R. Rondiya and K. Hareesh, J. Mater. Chem. A, 2025, Advance Article , DOI: 10.1039/D5TA04689K

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