Optimization of ultra-wideband metamaterial MXene-based polarization-insensitive solar absorber using machine learning for solar heater application.

The aim was to optimize energy production and minimize energy losses with regard to sources of sustainable energy, particularly solar energy, by examining a variety of solar absorber designs developed from various materials. Metamaterial MXene/W-based Resonator Solar Absorber (MMRSA) using MXene and Tungsten material utilized in a resonator, which has a tiny wire and cylindrical ring-shaped geometry. The MgF was utilized as a substrate and MXene was used as the ground layer of the suggested solar absorber. The MMRSA worked at the 200-3000 nm range and gained more than 94% absorptance. This MMRSA has a polarization-insensitive and ultra-wideband absorber, their wideband bandwidth is 1730 and 690 nm at 440 to 1930 nm and 1150 to 1840 nm. The negative metamaterial response such as permittivity, permeability, and refractive index given by the MMRSA increased the stability and absorptance of the absorber. To examine and optimize the MMRSA's different parameters and structure by examining the Transverse Electric and Magnetic properties. Optimized the MMRSA using machine learning which gives the higher value of R is 0.97779 and mean square error is 6.869962 × 10. Aims to reduce other simulation requirements thus minimizing simulation time by 25% when compared to previous approaches. Additionally, at last observed the MMRSA Electric and Magnetic intensity, and compared it with the previously studied absorber. The significant amount of absorptance with ultra-wideband this MMRSA is used for solar water heaters.