Next-Gen O-type solar thermal absorber engineered with graphene-based MXene–SiO2–MXene layers using machine learning techniques for industrial thermal applications

In this research, a next-generation plasmonic solar absorber has been designed utilizing advanced materials such as MXene and graphene. These materials were carefully selected due to their exceptional optical and electrical properties, contributing significantly to enhancing the absorber's performance. The design focuses on maximizing absorptance, with the structure achieving a peak absorption of 93% at a wavelength of 2800 nm. To further improve the efficiency of the absorber, various structural and material parameters were optimized. This optimization process ensures that the absorber operates effectively across the desired spectral range. In addition, machine learning algorithms were integrated into the design process to refine parameter selection and enhance overall absorptance. By leveraging data-driven techniques, the absorber’s performance was significantly improved, making it more suitable for advanced solar energy harvesting applications. This research successfully achieves a wide angle of incidence range from 0° to 50°, ensuring effective absorptance under varying sunlight directions for enhanced solar harvesting performance. This combination of innovative materials and intelligent optimization positions the design as a promising solution for next-generation photonic energy systems.