Exploration of Electrochemical Reactions at Organic–Inorganic Halide Perovskite Interfaces via Machine Learning in In Situ Time‐of‐Flight Secondary Ion Mass Spectrometry. (7th July 2020) - Higgins, Kate; Lorenz, Matthias; Ziatdinov, Maxim; Vasudevan, Rama K.; Ievlev, Anton V.; Lukosi, Eric D.; Ovchinnikova, Olga S.; Kalinin, Sergei V.; Ahmadi, Mahshid
Abstract: The instability of hybrid organic–inorganic perovskite (HOIP) devices is one of the significant challenges preventing commercialization. Exploring these phenomena is severely limited by the complexity of the intrinsic electrochemistry of HOIPs, the presence of multiple volatile and mobile ionic species, and the possible role of environmentally induced reactions at surfaces and triple‐phase junctions. Here, in situ studies of the electrochemistry of methylammonium lead bromide perovskite with the Au electrode interface are reported via light‐ and voltage‐dependent time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) imaging of lateral perovskite heterostructures. While ToF‐SIMS allows for the visualization of the chemical composition along the surface and its evolution with light and electrical bias, the interpretation of the multidimensional data obtained is often limited due to strong correlations between chemical signatures and the need to track multiple peaks at once. Here, a machine learning workflow combining the Hough transform and non‐negative matrix factorization and non‐negative tensor decomposition is developed to avoid this limitation and extract salient features of associated chemical changes and to separate the light‐ and voltage‐dependent dynamics. Combining these in situ characterizations and the machine learning workflow provides comprehensive information on the chemical nature of moving species, ion accumulation, and interfacial electrochemical reactions in HOIP devices. Abstract : This article reports a comprehensive analysis of the chemical nature of moving ions using time‐of‐flight secondary ion mass spectrometry in MAPbBr3 devices under operation condition. A machine learning workflow is developed to extract the salient features of associated chemical changes, visualizing the chemical transformations at the electrodes, and establishing the nature of the mobile species.