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Supercapacitors have become a research hotspot in the field of energy storage due to their highpower density, fast charging/discharging ability, long-term stability and safety. Nevertheless, relatively low energy density hindered their application. Herein, Ni3S2/Zn0.76Co0.24S (NZCS) microsphere were synthesized using a facile two-step hydrothermal process. The polymetallic synergies can improve conductivity and shorten ion transport path. The uniform particle distribution provided numerous active sites for faradaic reactions. The NZCS mircosphere showed a large capacity of 571.5 C g-1 at 1 A g-1 and 87% rate rentention when the current increases by 10 times. A hybrid supercapacitor assmebled by NZCS cathode and active carbon anode demostrate a high energy density of 44.1 Wh kg-1 (407.0 W kg-1) and a stable cycliability of 15,000 cycles with 15% loss. Thus, NZCS is a promising electrode material for high performance supercapacitor.

Semantic segmentation from a three‐dimensional point cloud is vital in autonomous driving, computer vision, and augmented reality. However, current semantic segmentation does not effectively use the point cloud's local geometric features and contextual information, essential for improving segmentation accuracy. A semantic segmentation network that uses local feature fusion and a multilayer attention mechanism is proposed to address these challenges. Specifically, the authors designed a local feature fusion module to encode the geometric and feature information separately, which fully leverages the point cloud's feature perception and geometric structure representation. Furthermore, the authors designed a multilayer attention pooling module consisting of local attention pooling and cascade attention pooling to extract contextual information. Local attention pooling is used to learn local neighbourhood information, and cascade attention pooling captures contextual information from deeper local neighbourhoods. Finally, an enhanced feature representation of important information is obtained by aggregating the features from the two deep attention pooling methods. Extensive experiments on large‐scale point‐cloud datasets Stanford 3D large‐scale indoor spaces and SemanticKITTI indicate that authors network shows excellent advantages over existing representative methods regarding local geometric feature description and global contextual relationships. [ABSTRACT FROM AUTHOR]

Single–atom catalysts (SACs) have attracted wide attention to be acted as potential electrocatalysts for nitrogen reduction reaction (NRR). However, the coordination environment of the single transition metal (TM) atoms is essential to the catalytic activity for NRR. Herein, we proposed four types of 3‐, 4‐coordinated and π–d conjugated TMxB3N3S6 (x=2, 3, TM=Ti, V, Cr, Mn, Fe, Zr, Nb, Mo, Tc, Ru, Hf, Ta, W, Re and Os) monolayers for SACs. Based on density functional theory (DFT) calculations, I‐TM2B3N3S6 and III‐TM3B3N3S6 are the reasonable 3‐coordinated and 4‐coordinated structures screening by structure stable optimizations, respectively. Next, the structural configurations, electronic properties and catalytic performances of 30 kinds of the 3‐coordinated I‐TM2B3N3S6 and 4‐coordinated III‐TM3B3N3S6 monolayers with different single transition metal atoms were systematically investigated. The results reveal that B3N3S6 ligand is an ideal support for TM atoms due to existence of strong TM‐S bonds. The 3‐coordinated I‐V2B3N3S6 is the best SAC with the low limiting potential (UL) of −0.01 V, excellent stability (Ef=−0.32 eV, Udiss=0.02 V) and remarkable selectivity characteristics. This work not only provides novel π–d conjugated SACs, but also gives theoretical insights into their catalytic activities and offers reference for experimental synthesis. [ABSTRACT FROM AUTHOR]