Unveiling degradation patterns in dye-sensitized solar cells: a machine learning perspective

Abstract

This study examines the time-dependent degradation of dye-sensitized solar cells (DSSCs) by systematically investigating several critical parameters, including TiO2 thickness, porosity, dye concentration, and iodine-based electrolyte concentration. We developed a novel figure of merit (FOM) to quantify the degradation rates, utilizing a finite element model (FEM) in COMSOL to simulate performance over time. 400 DSSC samples were fabricated, resulting in a comprehensive dataset comprising over 228,000 data points derived from experimental results and simulations. The findings reveal that PCE declines significantly over time, with an average initial efficiency of 4.0% for the DSSCs, dropping to approximately 0.5% after 360 h. The study utilizes Long Short-Term Memory (LSTM) models for training and validation, significantly enhancing the prediction of degradation behavior and yielding a correlation coefficient (R-2) of 0.92 when comparing predicted vs. observed efficiencies. This predictive capacity indicates the reliability of the LSTM model in assessing performance loss in DSSCs. The research underscores the complex interactions between the studied parameters and their cumulative effect on device longevity. Our results suggest that optimizing these areas can lead to more reliable DSSC designs. The novel degradation model and the established FOM facilitate future work in analyzing other solar cell technologies, particularly extending this methodology to emerging perovskite and organic solar cells for improved efficiency and durability in renewable energy applications.