This article presents a systematic review of the application of biomass-derived engineered biochar materials in high-performance supercapacitors, focusing on three key aspects: material preparation technologies, machine learning-assisted design, and commercialization prospects.

Firstly, the article elaborates on recent advancements in the preparation of high-performance electrode materials from biomass. Through a mechanistic analysis of three core processes—carbonization, activation, and heteroatom doping—it systematically compares the advantages and disadvantages of various preparation methods. Carbonization encompasses four main techniques: hydrothermal carbonization, direct carbonization, the template method, and microwave-assisted carbonization. Studies indicate that while hydrothermal carbonization operates at low temperatures with high energy efficiency, controlling the product properties remains challenging. Direct carbonization is operationally simple but energy-intensive. The template method allows precise modulation of pore structures but faces challenges such as template removal. Microwave-assisted carbonization efficiently produces porous carbon with hierarchical structures. In terms of activation, the article examines physical, chemical, and composite activation methods, with particular emphasis on the development of environmentally friendly activating agents. The section on heteroatom doping thoroughly discusses both self-doping and external doping strategies, highlighting how heteroatom incorporation effectively enhances the electrochemical performance of the materials.

Secondly, the article introduces machine learning methodologies to guide the design and preparation of high-performance carbon materials. Machine learning significantly improves material development efficiency through four key applications: data processing, performance prediction, process optimization, and full-process automation. Research indicates that tree-based algorithms can interpret models via feature importance calculations, offering new perspectives for understanding the complex relationships between material properties and electrochemical performance. The article also outlines the typical workflow of machine learning-based performance prediction.

Finally, the article discusses the major challenges and future development prospects in this field. These challenges include the unclear intrinsic relationship between the structural properties of engineered biochar and its electrochemical performance, the complexity of preparation processes, and the risk of secondary pollution. To address these issues, the article proposes the development of novel green and low-carbon synthesis methods and recommends comprehensive evaluations of technical processes using Life Cycle Assessment (LCA) and Techno-Economic Analysis (TEA). Regarding machine learning applications, the article suggests incorporating natural language processing, blockchain technology, active learning, and transfer learning to enhance data volume and quality, thereby mitigating the limitations posed by small and incomplete datasets.

The research outcomes presented in this article not only provide systematic theoretical guidance and technical support for the application of biomass-derived engineered biochar materials in supercapacitors but also introduce a new research paradigm through the integration of machine learning methodologies. These contributions hold significant theoretical and practical value for advancing the commercialization of high-performance supercapacitors.