Investigating the variation in the optical properties of TiO2 thin-film utilized in bifacial solar cells using machine learning algorithm

Abstract

Among various solar cell architectures, dye-sensitized solar cells (DSSCs) and perovskite solar cells have demonstrated the capability of being bifacial as both can be fabricated on conducting glass electrodes. In both cells, TiO2 plays a key role in the optoelectronic properties of the cell. Various studies have reported a range of recipes and deposition techniques for TiO2 thin films. Such variety introduces some uncertainties into the optical properties of the prepared films as well as in the process repeatability. Here, we utilized machine learning methods to correlate the film porosity to the film refractive index, making it capable of studying the impact of varying the fabrication and deposition techniques. Image postprocessing for scanning electron microscope measurements was utilized to estimate the film porosity, and the refractive index was calculated from the T–λ spectra. Four sets of samples with complete bifacial DSSCs were fabricated and characterized. They recorded a maximum current of 23.42 mA. They were fabricated using carboxymethyl cellulose-based suspension and deposited via the spin-coating sol-gel method. The fabricated cells showed an overall conversion efficiency of 7.9% under optical injection of the AM1.5G spectrum from the front side and LED indoor lighting from the counter electrode.