Triazole‐phosphine‐copper complexes (TAP−Cu) have been synthesized and applied as tunable and efficient catalysts for the selective synthesis of fluoro‐substituted 2‐aryl‐1H‐benzo[d]imidazole and 1‐benzyl‐2‐aryl‐1H‐benzo[d]imidazole derivatives from simple alcohols in only one step. TAP−Cu exhibited excellent and tunable catalytic activity for both dehydrogenation and borrowing hydrogen reactions with more than 80 examples being demonstrated for the first time. It was observed that the ligand played a critical role in catalyst activity. Mechanistic studies and deuterium labeling experiments indicated that the reactions proceeded by an initial and reversible alcohol dehydrogenation resulting in a copper hydride intermediate. This was also supported by the direct observation of a diagnostic copper hydride signal by solid‐state infrared spectroscopy. The TAP−Cu‐H complex showed absorptions at 912 cm−1 that could be assigned to copper−hydride stretches. Furthermore, the direct trapping of an intermediate bisimine was also successfully performed.magnified image

A BINAP-Cu system supported by hydrotalcite has been developed and proved to be a highly efficient catalyst for the atom-efficient and green borrowing hydrogen reaction and dehydrogenative cyclization.

MW ablation is a safe and effective technique for the management of hypersplenism in patients with liver cirrhosis. Ablating more than 40% of the splenic parenchyma may yield better long-term results.

Existing outdoor three-dimensional (3D) object detection algorithms mainly use a single type of sensor, for example, only using a monocular camera or radar point cloud. However, camera sensors are affected by light and lose depth information. When scanning a distant object or an occluded object, the data collected by the short-range radar point cloud sensor are very sparse, which affects the detection algorithm. To address the above challenges, we design a deep learning network that can combine the texture information of two-dimensional (2D) data and the geometric information of 3D data for object detection. To solve the problem of a single sensor, we use a reverse mapping layer and an aggregation layer to combine the texture information of RGB data with the geometric information of point cloud data and design a maximum pooling layer to deal with the input of multi-view cameras. In addition, to solve the defects of the 3D object detection algorithm based on the region proposal network (RPN) method, we use the Hough voting algorithm implemented by a deep neural network to suggest objects. Experimental results show that our algorithm has a 1.06% decrease in average precision (AP) compared to PointRCNN in easy car object detection, but our algorithm requires 37.7% less time to calculate than PointRCNN under the same hardware environment. Moreover, our algorithm improves the AP by 1.14% compared to PointRCNN in hard car object detection.

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