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CC BY-NC-ND V4.0, Open Access
Journal article
Machine Learning—Guided Design of Biomass-Based Porous Carbon for Aqueous Symmetric Supercapacitors
ChemPlusChem (Weinheim, Germany), Early View
2025
PMID: 40878512
Abstract
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Biomass-derived porous carbon electrodes have attracted significant attention for high-performance supercapacitor applications due to their sustainability, cost-effectiveness, and tunable porosity. To accelerate the design and evaluation of these materials, it is essential to develop accurate and efficient strategies for optimizing their physicochemical and electrochemical properties. Herein, a machine learning (ML) approach is employed to analyze experimental data from previously reported sources, enabling the prediction of specific capacitance (F g−1) based on various material characteristics and processing conditions. The trained ML model evaluates the influence of factors such as biomass type, electrolyte, activating agent, and key synthesis parameters, including activation and carbonization temperatures and durations, on supercapacitor performance. Despite growing interest, comprehensive studies that correlate these variables with performance metrics remain limited. This work addresses this gap by using ML algorithms to uncover the interrelationships between biomass-derived carbon properties, synthesis conditions, and specific capacitance. Herein, it is demonstrated that an optimal combination of a carbonized honeydew peel to H3PO4 ratio of 1:4 and an activation temperature of 500 °C yields a highly porous carbon material. When used in a symmetric device with 1 M H2SO4 electrolyte, this material, rich in oxygen and phosphorus species, achieves a high specific capacitance of 611 F g−1 at a current density of 1.3 A g−1. Correlation analysis reveals a strong synergy between surface area and pore volume (correlation coefficient = 0.8473), and the ML-predicted capacitance closely aligns with experimental results. This ML-assisted framework offers valuable insights into the critical physicochemical and electrochemical parameters that govern supercapacitor performance, providing a powerful tool for the rational design of next-generation energy storage materials.
Details
- Title
- Machine Learning—Guided Design of Biomass-Based Porous Carbon for Aqueous Symmetric Supercapacitors
- Authors/Creators
- Manickam Minakshi - Murdoch University, Centre for Water, Energy and WasteApsana Sharma - College of Science, Technology, Engineering & Mathematics, Murdoch University, Murdoch, WA, AustraliaFerdous Sohel - Murdoch University, Centre for Crop and Food InnovationAlmantas Pivrikas - Murdoch University, School of Mathematics, Statistics, Chemistry and PhysicsPragati A. Shinde - National Institute for Materials ScienceKatsuhiko Ariga - National Institute for Materials ScienceLok Kumar Shrestha - National Institute for Materials Science
- Publication Details
- ChemPlusChem (Weinheim, Germany), Early View
- Publisher
- Wiley-VCH GmbH
- Number of pages
- 13
- Grant note
- JP20H00392; JP23H05459 / Japan Society for the Promotion of Science (http://data.elsevier.com/vocabulary/SciValFunders/501100001691) Japan Society for the Promotion of Science (http://data.elsevier.com/vocabulary/SciValFunders/501100001691)
- Identifiers
- 991005810349707891
- Copyright
- © 2025 The Author(s).
- Murdoch Affiliation
- Centre for Crop and Food Innovation; School of Mathematics, Statistics, Chemistry and Physics; School of Information Technology; Centre for Water, Energy and Waste; College of Science, Technology, Engineering and Mathematics
- Language
- English
- Resource Type
- Journal article
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InCites Highlights
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- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 2 Chemistry
- 2.62 Electrochemistry
- 2.62.52 Electrode Materials
- Web Of Science research areas
- Chemistry, Multidisciplinary
- ESI research areas
- Chemistry