Speaker
Description
Perovskite solar cells have emerged as one of the most promising next-generation photovoltaic technologies, reaching efficiencies comparable to established thin-film and silicon-based devices while offering low-temperature processing, lightweight architectures, and tunable optoelectronic properties. These features make them especially attractive for space photovoltaic applications, where high specific power, low mass, radiation tolerance, and compatibility with flexible substrates are highly desirable. However, long-term stability remains a central challenge, particularly under operational stressors such as illumination, heat, humidity, bias, vacuum, radiation, and thermal cycling. In this contribution, I will discuss how in situ and synchrotron-based characterization can reveal the structural, chemical, and electronic processes that govern stability and degradation in perovskite photovoltaic devices. The broader field has shown that device failure under humidity or thermal stress can be driven by interfacial reactions, ion migration, electrode corrosion, and surface-initiated degradation processes. Building on this context, the presentation will be based on our previous studies using in situ GIWAXS to investigate perovskite film formation by antisolvent and gas-quenching methods, showing how precursor chemistry, solvent coordination, intermediate phases, and processing conditions influence crystallization pathways, morphology, and device performance. Additional examples will include our studies on degradation mechanisms in mixed-cation and mixed-halide perovskite films under ambient conditions, the influence of reduced graphene oxide on perovskite film formation and device stability, and recent results on polytype selection and ambient crystallization dynamics in formamidinium lead iodide systems. Together, these studies show that degradation in perovskite photovoltaics is strongly linked to phase evolution, ion migration, surface and grain-boundary chemistry, interfacial reactions, and local structural heterogeneity.
Because in situ and operando experiments generate complex multidimensional datasets, data-driven approaches, including machine learning and pattern-recognition methods, can accelerate the identification of degradation signatures, correlate processing conditions with structural evolution, and extract hidden structure-property-stability relationships. By connecting advanced characterization with data-driven analysis, this presentation highlights perovskite photovoltaics as a model platform for understanding degradation in functional materials, with relevance to space technologies, sensors, and scientific instrumentation operating under harsh environments.