Simulation‐Based Investigation of Thermal Loss Reduction and Efficiency Recovery in CdTe Solar Cells Using Polyacrylamide Hydrogel

Operation of Cadmium Telluride (CdTe) solar cells at high temperature causes significant thermal losses, becoming a barrier in reaching peak efficiency. To address this challenge, we offer a simulation‐based novel approach using a highly efficient polyacrylamide (PAM) hydrogel‐based passive cooling system. This proposed device was optimized by using a multi‐software computational framework solar cell that combined density functional theory (DFT) for material input, SCAPS‐1D for device simulation, and machine learning using the XGBoost algorithm with a coefficient of determination of R ²  = 0.98, for calculating temperature decrease using PAM Hydrogel. The proposed optimized CdTe‐based solar cell has a functional architecture of SnO ₂ /CdS/CdTe/Al with a remarkable PCE of 24.67%, a J _SC of 24.29 mA/cm ² , a V _OC of 1.16 V, and an FF of 87.17% at standard test conditions. A machine learning model was used to study temperature decrease and photovoltaic metrics improvements due to the application of PAM. A substantial decrease in the temperature of the CdTe solar cell by 18°C was observed. This decrease in operational temperature leads to improvements in photovoltaic metrics.