3D carbon-based MXene composite electrodes for supercapacitors: Synthesis strategies, hybrid architectures, and machine-learning-guided design

Abstract

Supercapacitors (SCs) are critical for high-power energy storage, yet their practical deployment is still limited by insufficient energy density. MXenes have emerged as promising electrode materials owing to inherent metallic conductivity, hydrophilic terminations, and intercalation pseudocapacitance, but suffer from layer restacking and oxidative degradation. Hybridizing MXenes with 3D carbon scaffolds offers a synergistic strategy by providing interlayer spacers, hierarchical ion transport pathways, and oxidation barriers. To the best of our knowledge, this review is the first to systematically couple the structural design of MXene/3D-carbon composites with machine learning (ML) guided optimization, summarizing recent advances in MXene/3D-carbon composite electrodes. This review systematically evaluates MXene etching routes and representative composite assembly strategies, and classifies existing systems into three structural categories, critically comparing their performance metrics, strengths, and inherent limitations. It further highlights the frontier applications of ML in performance prediction, compositional optimization, and mechanical design, along with current challenges, including data bias, the black-box nature of models, and the gap between idealized predictions and real synthesis. Overall, this work aims to provide a framework for integrating advanced synthesis strategies, 3D architectures, and data-driven tools toward the rational design of high-performance SCs.

Keywords

MXene / 3D carbon materials / supercapacitor / composite electrode / machine learning

Acknowledgements

The authors declare that an AI-based assistant (Claude, Anthropic) was used solely for language polishing to improve the readability and grammar of the English text during manuscript preparation. All research content, including the study scope and design, literature analysis, interpretation of results, conclusions, figures, and tables, is the authors’ original work and was not generated using AI tools. The authors reviewed and edited all AI-assisted text and take full responsibility for the content of the manuscript.

Authors contribution

Mu B: Conceptualization, writing-original draft, writing-review & editing, visualization, formal analysis. Hou Z: Investigation, formal analysis. Shi S: Investigation, visualization.

Conflicts of interest

The authors declare no conflicts of interest.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

Not applicable.

Funding

This work was supported by the Guangxi Universities Young and Middle-aged Teachers’ Research Basic Ability Improvement Project (Grant No. 2024KY0396).

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