Organic-inorganic perovskite materials are revolutionizing photovoltaics with high power conversion efficiencies, but experience significant environmental degradation and instability. In this work, the phase stability and decomposition mechanisms of lead-free all inorganic Cs₂SnI₆ perovskite upon water and moisture exposure were systematically investigated via in situ synchrotron X-ray diffraction, environmental SEM, and micro-Raman spectroscopy. A critical relative humidity (80%) is identified below which Cs₂SnI₆ perovskite is stable without decomposition. Under higher humidity or aqueous environment, Cs₂SnI₆ perovskite decomposes into SnI₄ and CsI through etch pits formation and stepwave propagation, leading to rapid crystal dissolution. A partial reversibility of the Cs₂SnI₆ perovskite upon dissolution and re-precipitation with subsequent dehydration was identified, suggesting a self-healing capability of Cs₂SnI₆ and thus enhanced air stability. Mechanistic understanding of the Cs₂SnI₆ degradation behavior can be a vital step towards developing new perovskites with enhanced environmental stability and materials performance.

Online social media provides massive open-ended platforms for users of a wide variety of backgrounds, interests, and beliefs to interact and debate, facilitating countless discussions across a myriad of subjects. With numerous unique voices being lent to the ever-growing information stream, it is essential to consider how the types of conversations that result from a social media post represent the post itself. We hypothesize that the biases and predispositions of users cause them to react to different topics in different ways not necessarily entirely intended by the sender. In this paper, we introduce a set of unique features that capture patterns of discourse, allowing us to empirically explore the relationship between a topic and the conversations it induces. Utilizing “microscopic” trends to describe “macroscopic” phenomena, we set a paradigm for analyzing information dissemination through the user reactions that arise from a topic, eliminating the need to analyze the involved text of the discussions. Using a Reddit dataset, we find that our features not only enable classifiers to accurately distinguish between content genre, but also can identify more subtle semantic differences in content under a single topic as well as isolating outliers whose subject matter is substantially different from the norm.