A defect passivation strategy using [BMMIm]Cl ion liquid is demonstrated for the fabrication of high-performance, charge-transport-layer-free CsPbCl₃ UV photodetectors, achieveing an ultrahigh on/off ratio of 2.70 × 10⁵ at 0 V bias.

ChCl is introduced to passivate the surface and grain boundary defects of CsPbCl₃films for their application in high-performance UV detection and an ultrahigh on/off ratio of 3.91 × 10⁵is obtained at 0 V bias.

The decline of point cloud registration efficiency caused by bad initial position and disordered registration direction has not been effectively solved. Herein, we propose a robust registration algorithm to tackle these drawbacks. First, a novel automatic point cloud alignment strategy considering the normal vector of feature points is demonstrated. This strategy ensures fast convergence in the case of bad initial position. Second, we introduce a cross iterative optimization strategy, which combines the alignment algorithm with an improved ICP (Point-Surface ICP) version based on surface constraints to complete faster and more orderly registration. In order to reduce the computational complexity, we present a linearization for the Point-Surface ICP based on Rodrigues rotation parameterization with the small incremental rotation assumption. In the elimination of outliers, we use the normal distribution of multiple errors to automatically select the threshold interval. Eventually, a large number of experiments are conducted on some public data-sets for performance evaluation of the as-proposed algorithm. Compared with other optimal methods, our method achieves a 17.1 $\%$ and 58.98 $\%$ increase in registration accuracy in Dragon dataset and Armadillo dataset, respectively, indicating the higher superiority of our algorithm. Note to Practitioners —This paper was motivated by solving the problem of registering two PCs. Most existing approaches generally can’t solve the decline of point cloud registration efficiency caused by bad initial position and disordered registration direction. In this paper, the position information and normal vector information of feature points are considered as the constraint conditions of pose alignment, and the improved ICP is used for further registration. In order to reduce the influence of outliers, an adaptive comprehensive elimination condition is proposed. We have demonstrated through extensive experiments that the proposed registration algorithm achieves improved accuracy, robustness to point clouds of different scales, and faster convergence speed.

Converting biowaste into carbon-based supercapacitor materials provides a new solution for high-performance and environmentally friendly energy storage applications. Herein, the hierarchical PAC/NiCo₂S₄ composite structure was fabricated through the combination of activation and sulfuration treatments. The PAC/NiCo₂S₄ electrode garnered advantages from its hierarchical structure and hollow architecture, resulting in a notable specific capacitance (1217.2 F g⁻¹ at 1.25 A g⁻¹) and superior cycling stability. Moreover, a novel all-solid-state asymmetric supercapacitor (ASC) was successfully constructed, utilizing PAC/NiCo₂S₄ as the cathode and PAC as the anode. The resultant device exhibited exceptionally high energy (49.7 Wh kg⁻¹) and power density (4785.5 W kg⁻¹), indicating the potential of this biomass-derived, hierarchical PAC/NiCo₂S₄ composite structure for employment in high-performance supercapacitors.

Recently, deep-learning-based methods have achieved excellent performance in semiconductor wafer defect inspection. However, these trained networks are only effective for a certain background pattern and fail in defect transfer detection between multiple background patterns. Once the background pattern has changed, images are needed to be recollected and relabeled, which is very time-consuming. To achieve defect transfer detection in semiconductor wafers, a novel prototype learning (PL)-based network is proposed in this article. First, a multiscale residual fusion module (MSRFM) is elaborately designed to generate feature maps that minimize the interference of background patterns. Second, some labeled defect images are chosen as a defect library, which is further extracted into a defect prototype. Then, a distance map will be calculated between feature maps and the defect prototype, resulting in a precise prediction. Finally, an extra self-constraint loss ( $L_{\text {sc}}$ ) is designed to regulate the defect prototype calculation process. Different from previous methods, only defect-free images are required for the proposed method to achieve defect transfer detection. Experiments on real-world semiconductor wafer production lines show that the proposed method achieves mean intersection over union (mIoU) of 83.49% and 80.12% in defect transfer detection between two background pattern wafers. Furthermore, the excellent performance on other classical industrial datasets demonstrates that the proposed network has great robustness to various defects and industrial scenarios.