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<p>The urgent need for sustainable and energy-efficient technologies to address escalating environmental pollution and energy demand has driven significant interest in photocatalysis. Carbon-based nanomaterials such as graphene, carbon nanotubes, and carbon dots are widely explored for photocatalytic applications. They show high surface area, tunable electronic properties, and good chemical stability. These features make them suitable for reactions driven by light. However, many conventional synthesis methods, including hydrothermal, solvothermal, pyrolytic, and chemical vapour deposition techniques, require high energy input and toxic chemicals. Additionally, limited control over material properties in these approaches restricts their effectiveness in achieving targeted photocatalytic performance. This work focuses on alternative synthesis routes that are more sustainable and controllable. Less explored approaches such as sonochemical, microwave-assisted, mechanochemical, electrochemical, plasma-assisted, and biomass-derived synthesis are discussed. These methods offer better control over morphology, heteroatom doping, and hybrid structure formation. Such control is essential for improving charge transfer, light absorption, and catalytic efficiency. The role of these engineered carbon nanomaterials in photocatalytic degradation of organic pollutants, carbon dioxide reduction, and water splitting is examined. Several challenges still limit practical application. These include material stability, batch-to-batch reproducibility, and integration into real systems. Scaling up production while maintaining performance remains difficult. The use of artificial intelligence and machine learning is briefly explored as a tool to guide synthesis design and optimise process conditions. Sustainable synthesis strategies, combined with emerging tools such as artificial intelligence-driven design, are expected to play a crucial role in enabling scalable, high-performance carbon-based photocatalysts for future energy and environmental applications.</p> Graphical abstract <p></p>

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Sustainable synthesis, doping and hybridization strategies in carbon nanomaterials for photocatalytic energy and environmental applications

  • 期刊论文

  • 发表日期：

    2026年6月16日

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  • Lovepreet Singh,
  • Sakshi Sharma,
  • Wisdom Archford Munzeiwa,
  • Shaweta Sharma,
  • Masukur Rahman,
  • Harshita Jain

摘要

The urgent need for sustainable and energy-efficient technologies to address escalating environmental pollution and energy demand has driven significant interest in photocatalysis. Carbon-based nanomaterials such as graphene, carbon nanotubes, and carbon dots are widely explored for photocatalytic applications. They show high surface area, tunable electronic properties, and good chemical stability. These features make them suitable for reactions driven by light. However, many conventional synthesis methods, including hydrothermal, solvothermal, pyrolytic, and chemical vapour deposition techniques, require high energy input and toxic chemicals. Additionally, limited control over material properties in these approaches restricts their effectiveness in achieving targeted photocatalytic performance. This work focuses on alternative synthesis routes that are more sustainable and controllable. Less explored approaches such as sonochemical, microwave-assisted, mechanochemical, electrochemical, plasma-assisted, and biomass-derived synthesis are discussed. These methods offer better control over morphology, heteroatom doping, and hybrid structure formation. Such control is essential for improving charge transfer, light absorption, and catalytic efficiency. The role of these engineered carbon nanomaterials in photocatalytic degradation of organic pollutants, carbon dioxide reduction, and water splitting is examined. Several challenges still limit practical application. These include material stability, batch-to-batch reproducibility, and integration into real systems. Scaling up production while maintaining performance remains difficult. The use of artificial intelligence and machine learning is briefly explored as a tool to guide synthesis design and optimise process conditions. Sustainable synthesis strategies, combined with emerging tools such as artificial intelligence-driven design, are expected to play a crucial role in enabling scalable, high-performance carbon-based photocatalysts for future energy and environmental applications.

Graphical abstract