Smart design of graphene-Al-W-AlSb absorbers for next-gen ultrabroadband solar energy capture using machine learning
Meshari Alsharari, Bo Bo Han, Ammar Armghan, Khaled Aliqab, Shobhit K. Patel
Abstract
As a solution to air pollution, renewable energy systems have gained increasing attention for power generation, with solar energy playing a major role. Solar absorbers, essential components in solar energy systems, are widely used for thermal processes in heating systems. Various types of absorbers are explored to achieve optimal efficiency, and the three-layer structure is the most popular due to its cost-effectiveness and high absorption efficiency. The existing structure consists of three absorber sections made of aluminum, tungsten, and aluminum antimonide, with a thin layer of graphene. The overall study, based on wavelength and band rates, shows results such as 740 nm (0.92–1.66 μm) achieving over 97 %, 1460 nm (0.20–1.66 μm) achieving over 95 %, and 2800 nm (overall) achieving 91.85 %, with machine learning (linear regression) performance identified across different air sections. The current broadband absorber can be primarily applied in a variety of thermal applications, including heating processes for solar space, industrial systems, swimming pools, agriculture, and solar water heating systems, among others.