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Machine learning-enhanced ultrawideband Al-In₂Se₃-AlN solar absorber investigation for heating water systems using thin-layer graphene

  • Shobhit K. Patel*
  • , Bo Bo Han
  • , Osamah Alsalman*
  • , Om Prakash Kumar*
  • *Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    Abstract

    With the perfect absorption of the constructed structure in building a solar absorber, it can achieve the role of solar energy (green energy system). In constructing the perfect solar design, the applied material types and the number of layers need to be considered. Some additional layers of MXene, graphene, and so on can also be used to produce an ideal type of solar structure. In the current contributed solar design, a three-layer and a graphene performance combination with the materials of Aluminium (Al) for the resonator, the substrate material is Indium (III) selenide (In2Se3), and the whole structure is based on the Aluminium nitride (AlN). The wavelengths of 530 and 650 nm produce above 95 % absorption rates of 95.56 % and 95.29 % and the wide wavelength of 2800 nm generates 91.91 % as the current work's outputs. The results (absorption rates) on the descriptive parametric number can be generated by the applied ML (machine learning). In some thermal processes of the domestic sector, mandatory uses, non-commercial sectors, and generating steam and heating of oil systems can be performed with the current displayed solar design.

    Original languageEnglish
    Article number122513
    JournalRenewable Energy
    Volume243
    DOIs
    Publication statusPublished - 15-04-2025

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 7 - Affordable and Clean Energy
      SDG 7 Affordable and Clean Energy

    All Science Journal Classification (ASJC) codes

    • Renewable Energy, Sustainability and the Environment

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