Design and optimization of machine learning-based Cr-TiN-AlSb solar absorber based on graphene material for renewable energy applications

The main goal of addressing the inefficiency of electricity and reducing air pollution faced today has led to the invention of the recent absorber as a form of green energy. While investigating the absorber with more layers could improve its performance, the three-layer type remains the most widely used. Additionally, the inclusion of a graphene layer enhances the efficiency, making it a wideband structure. The current structure employs a three-layer absorber technique, consisting of Chromium, Titanium Nitride (TiN), and Aluminum Antimonide (AlSb) (from the resonance surface to the base layer). With an overall efficiency of 93 % over a 2800 nm bandwidth, the output efficiencies can be studied across different air regions (UV – NIR). Our investigated absorber is a broadband type, suitable for a variety of heating applications, both industrial and domestic. Furthermore, this broadband absorber can be utilized to collect and emit heat energy at high temperatures.