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Abstract Cesium methylammonium lead iodide (Cs_xMA_1−xPbI₃) nanocrystals were obtained with a wide range of A‐site Cs‐MA compositions by post‐synthetic, room temperature cation exchange between CsPbI₃nanocrystals and MAPbI₃nanocrystals. The alloyed Cs_xMA_1−xPbI₃nanocrystals retain their photoactive perovskite phase with incorporated Cs content,x, as high as 0.74 and the expected composition‐tunable photoluminescence (PL). Excess methylammonium oleate from the reaction mixture in the MAPbI₃nanocrystal dispersions was necessary to obtain fast Cs‐MA cation exchange. The phase transformation and degradation kinetics of films of Cs_xMA_1−xPbI₃nanocrystals were measured and modeled using an Avrami expression. The transformation kinetics were significantly slower than those of the parent CsPbI₃and MAPbI₃nanocrystals, with Avrami rate constants,k, at least an order of magnitude smaller. These results affirm that A‐site cation alloying is a promising strategy for stabilizing iodide‐based perovskites. 

Single-element detectors (SEDs) with a room temperature extended short-wave infrared (eSWIR) photoresponse were fabricated with branched nanorods of HgTe. Nanorods with high aspect (length/width) ratios were obtained by using stoichiometric excesses of Hg (i.e., [Hg]/[Te] molar ratios greater than one) and relatively low reaction temperatures, as low as room temperature. The size-tunable optical cutoff wavelengths of the detectors ranged from 2 to 3.5 μm, with specific detectivities as high as 2.4 × 10^11 Jones. The devices retained their responsivity for more than a year. Branched nanorods of HgTe are promising materials for IR photodetectors and imagers. 

We report a data-parsimonious machine learning model for short-term forecasting of solar irradiance. The model follows the convolutional neural network – long-short term memory architecture. Its inputs include sky camera images that are reduced to scalar features to meet data transmission constraints. The model focuses on predicting the deviation of irradiance from the persistence of cloudiness (POC) model. Inspired by control theory, a noise signal input is used to capture the presence of unknown and/or unmeasured input variables and is shown to improve model predictions, often considerably. Five years of data from the NREL Solar Radiation Research Laboratory were used to create three rolling train-validate sets and determine the best representations for time, the optimal span of input measurements, and the most impactful model input data (features). For the chosen validation data, the model achieves a mean absolute error of 74.29 W/m2 over a time horizon of up to two hours, compared to a baseline 134.35 W/m2 using the POC model.