Understanding perovskite degradation and stress responses under practical conditions is necessary to design efficient and stable photovoltaic devices. This experiment creates an in-situ stress testing system, in which the stress of a sample may be tested while it is subjected to conditions that it may experience in operation, such as cycles of sunlight. This immediate stress response is valuable in understanding what factors directly contribute to the stresses that degrade perovskite solar cells. Perovskite may be 2D or 3D and are composed of many different elements and additives. Each ink responds differently to sunlight exposure due to their different structures, which is important to characterize and comprehend. Preliminary testing and characterization for 2D, 3D and MAPI with gellan gum additive perovskite inks is conducted in this experiment. It is reported that the lattice expansion causing degradation-inducing stress is due to photon dosage rather than heat, and both 3D and 2D perovskites are sensitive to minute photon dosage.

To reduce the cost of silicon solar cells and improve their efficiency, it is crucial to identify and understand the defects limiting the electrical performance in silicon wafers. Bulk defects in semiconductors produce discrete energy levels within the bandgap and may act as recombination centers. This project investigates the viability of using machine learning for characterizing bulk defects in Silicon by using a Random Forest Regressor to extract the defect energy level and capture cross section ratios for a simulated Molybdenum defect and experimental Silicon Vacancy defect. Additionally, a dual convolutional neural network is used to classify the defect energy level in the upper or lower half bandgap.

With the rise of global warming and the growing energy crisis, scientists have pivoted from typical resources to look for new materials and technologies that can aid in advancing renewable energy efforts. Perovskite materials hold the potential for making high-efficiency, low-cost solar cells through solution processing of Earth abundant materials; however, scalability and manufacturability remain a challenge. In order to transition from small scale processing in inert environments via spin coating to higher throughput processing in ambient conditions via blade coating, the fundamentals of perovskite crystallization must be understood. Classical nucleation theory, the LaMer relation, and nonclassical crystallization considerations are discussed to provide a mechanism by which gellan gum, a nontoxic biopolymer from the food industry, has enabled quality halide perovskite thin films. Specifically, this research aims to study the effects of gellan gum in improving perovskite manufacturability by controlling crystallization through indirect alteration of evaporation and supersaturation rates by modifying fluid dynamics and the free energy associated with nucleation and growth. Simply, gellan gum controls crystallization to enable the fabrication of promising scalable PVSK devices in open air.