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We report the palladium-catalyzed gem-difluoroallylation of aryl halides and pseudo halides with 3,3-difluoroallyl boronates in high yield with high regioselectivity, and we report the preparation of the 3,3-difluoroallyl boronate reactants by a copper-catalyzed defluorinative borylation of inexpensive gaseous 3,3,3-trifluoropropene with bis(pinacola-to) diboron. The gem-difluoroallylation of aryl and heteroaryl bromides proceeds with low catalyst loading (0.1 mol% [Pd]) and tolerates a wide range of functional groups, including primary alcohols, secondary amines, ethers, ketones, esters, amides, aldehydes, nitriles, halides, and nitro groups. This protocol extends to aryl iodides, chlorides, and triflates, as well as substituted difluoroallyl boronates, providing a versatile synthesis of gem-difluoroallyl arenes that we show to be valuable intermediates to a series of fluorinated building blocks 

The pixel-wise code exposure (PCE) camera is a compressive sensing camera that has several advantages, such as low power consumption and high compression ratio.Moreover, one notable advantage is the capability to control individual pixel exposure time. Conventional approaches of using PCE cameras involve a time-consuming and lossy process to reconstruct the original frames and then use those frames for target tracking and classification. Otherwise, conventional approaches will fail if compressive measurements are used. In this paper, we present a deep learning approach that directly performs target tracking and classification in the compressive measurement domain without any frame reconstruction. Our approach has two parts: tracking and classification. The tracking has been done via detection using You Only Look Once (YOLO), and the classification is achieved using residual network (ResNet). Extensive simulations using short-wave infrared (SWIR) videos demonstrated the efficacy of our proposed approach. 

Surface trap–mediated nonradiative charge recombination is a major limit to achieving high-efficiency metal-halide perovskite photovoltaics. The ionic character of perovskite lattice has enabled molecular defect passivation approaches through interaction between functional groups and defects. However, a lack of in-depth understanding of how the molecular configuration influences the passivation effectiveness is a challenge to rational molecule design. Here, the chemical environment of a functional group that is activated for defect passivation was systematically investigated with theophylline, caffeine, and theobromine. When N-H and C=O were in an optimal configuration in the molecule, hydrogen-bond formation between N-H and I (iodine) assisted the primary C=O binding with the antisite Pb (lead) defect to maximize surface-defect binding. A stabilized power conversion efficiency of 22.6% of photovoltaic device was demonstrated with theophylline treatment.