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<p>Solar absorbers are essential components in solar thermal energy systems, as they directly capture and convert sunlight into heat while aiming to achieve high efficiency with minimal thermal losses. The engineering of multilayer absorber structures offers a promising approach to maximize spectral absorption performance across a wide range of incident radiation. In this study, a graphene-integrated tri-layer absorber based on Cr-Ti-TiC materials is designed to enhance optical absorption and thermal conversion efficiency. The proposed structure demonstrates remarkable broadband absorption, achieving 97.49% absorption at 1000&#xa0;nm and 94.13% absorption at 2800&#xa0;nm, covering the ultraviolet, visible, and infrared wavelength regions. To further improve performance and ensure optimal design parameters, machine learning regression techniques are applied, enabling accurate prediction of absorber behavior and supporting intelligent geometric optimization. The comprehensive computational results indicate that the developed absorber is well suited for advanced solar thermal applications, offering significant potential for both household and industrial energy systems. Additionally, this work provides valuable insights into scalable fabrication approaches for multilayer graphene-based absorbers, contributing to future research and development in renewable energy harvesting technologies.</p>

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Graphene-enhanced Cr-Ti-TiN broadband absorber for solar thermal systems: a computational and machine learning approach

  • 期刊论文

  • 发表日期：

    2026年2月17日

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  • Khaled Aliqab,
  • Bo Bo Han,
  • Ashish Baldania,
  • Ammar Armghan,
  • Meshari Alsharari,
  • Shobhit K. Patel

摘要

Solar absorbers are essential components in solar thermal energy systems, as they directly capture and convert sunlight into heat while aiming to achieve high efficiency with minimal thermal losses. The engineering of multilayer absorber structures offers a promising approach to maximize spectral absorption performance across a wide range of incident radiation. In this study, a graphene-integrated tri-layer absorber based on Cr-Ti-TiC materials is designed to enhance optical absorption and thermal conversion efficiency. The proposed structure demonstrates remarkable broadband absorption, achieving 97.49% absorption at 1000 nm and 94.13% absorption at 2800 nm, covering the ultraviolet, visible, and infrared wavelength regions. To further improve performance and ensure optimal design parameters, machine learning regression techniques are applied, enabling accurate prediction of absorber behavior and supporting intelligent geometric optimization. The comprehensive computational results indicate that the developed absorber is well suited for advanced solar thermal applications, offering significant potential for both household and industrial energy systems. Additionally, this work provides valuable insights into scalable fabrication approaches for multilayer graphene-based absorbers, contributing to future research and development in renewable energy harvesting technologies.