Improvement of Perovskite Solar Cell Efficiency through PLA Additive Induced Boundary Passivation : With Application of Machine Learning in Crystal Image Analysis

Abstract

Perovskite solar cells are an extremely promising photovoltaic technology due to their rapid improvement in power conversion efficiency (PCE) in recent years. Polylactic acid (PLA) is a biodegradable polyester plastic that is used in additive manufacturing. In this study, we used two PLA additives, 30K PLA and 16K PLA, to improve the performance of perovskite solar cells. In particular, we show that these PLA additives unanimously increase the PCE of perovskite solar cells across various concentrations and molecular weights. Additionally, because perovskite crystal size is positively correlated with cell efficiency, we developed a machine learning algorithm to autonomously compute size distributions of PLA-modified perovskite crystals from SEM images. This procedure showed that both the 30K and 16K PLA increased the average size of the crystal grains (p 2 , with PLA at the core and a PbI 2 surrounding. These complexes passivate the boundaries between crystal grains, decreasing the number of trapped or dissoluted excitons and redirecting them in the direction of the current. The complexes likely also decrease the interfacial tension between crystal grains, resulting in crystal sizes which are up to 226% larger. By varying only concentration or only molecular weight, we discovered that there existed an optimal concentration and an optimal molecular weight of around 0.3 mg/mL and 16,000 amu respectively. We identified competing factors that would yield this trend: the aforementioned positive effects of the PLA, and the negative effects of nonideality of the JV curve (and consequences) and poorer conductivity. Given the observed improvement in efficiency of up to 17.5%, these results demonstrate that PLA additives are a novel and promising method of improving the performance of perovskite solar cells.