Hybrid organic–inorganic perovskites exhibit extraordinary photovoltaic performance. This is believed to arise from almost liquid-like low-energy interactions among lattice ions and charge carriers. While spatial variations have recently been identified over multiple length scales in the optoelectronic response of perovskites, the relationship between the heterogeneity and the soft cation–lattice interactions has remained elusive. Here, we apply multivariate infrared vibrational nanoimaging to a formamidinium (FA)–methylammonium (MA)–cesium triple-cation perovskite by using the FA vibrational resonance as a sensitive probe of its local chemical environment. The derived correlation among nanoscale composition, cation–lattice coupling, and associated few-picosecond vibrational dynamics implies a heterogeneous reaction field and lattice contraction that we attribute to a spatially nonuniform distribution of cesium cations. The associated spatial variation in elasticity of the lattice leads to disorder in charge–phonon coupling and related polaron formationthe control of which is central to improving perovskite photovoltaics.

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Soft sensors are data-driven devices that allow for estimates of quantities that are either impossible to measure or prohibitively expensive to do so. DL (deep learning) is a relatively new feature representation method for data with complex structures that has a lot of promise for soft sensing of industrial processes. One of the most important aspects of building accurate soft sensors is feature representation. This research proposed novel technique in automation of manufacturing industry where dynamic soft sensors are used in feature representation and classification of the data. Here the input will be data collected from virtual sensors and their automation-based historical data. This data has been pre-processed to recognize the missing value and usual problems like hardware failures, communication errors, incorrect readings, and process working conditions. After this process, feature representation has been done using fuzzy logic-based stacked data-driven auto-encoder (FL_SDDAE). Using the fuzzy rules, the features of input data have been identified with general automation problems. Then, for this represented features, classification process has been carried out using least square error backpropagation neural network (LSEBPNN) in which the mean square error while classification will be minimized with loss function of the data. The experimental results have been carried out for various datasets in automation of manufacturing industry in terms of computational time of 34%, QoS of 64%, RMSE of 41%, MAE of 35%, prediction performance of 94%, and measurement accuracy of 85% by proposed technique.

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