Perovskites for Energy Harvesting

The extraordinary performance of perovskite solar cells (> 20%) is still hampered by their dynamic optical and electrical responses, which often lead to degradation. The individual and combined effects of water, oxygen, temperature, bias, and light must be controlled for their future commercialization. To unravel the contribution of each parameter on materials’ properties and devices’ performance, we combine advanced scanning probe methods. We investigate a series of hybrid perovskites, including MAPbI₃, MAPbBr₃, CsxFA1−xPb(IyBr1−y)3 , and triple cation Cs-mixed. Using environmental PL microscopy we elucidate a humidity-induced PL hysteresis, strongly dependent on the Cs/Br ratio. Through Kelvin-probe force microscopy we elucidate the dynamic open-circuit voltage response as a function of chemical composition and illumination treatments. We propose a machine learning (ML) paradigm to identify the influence of each aforementioned parameter on perovskite’s stability and device performance. Our functional microscopy platform, combined with ML, can be leveraged to assess Pb-free perovskites.