Tracking and grasping moving objects are hot topics in the field of robots, which provides great potential in the industrial scene and human–computer cooperation. Based on the kernel correlation filter and vision 3-D reconstruction, this article proposes a visual-based tracking and grasping method for moving targets. An improved algorithm based on the kernel correlation filter is proposed for object tracking. A scale pool is constructed, and a scale filter is trained to solve the problem of algorithm scale adaptation. At the same time, the judgment mechanism of tracking results, secondary detection, and modification update mechanisms are added to improve the robustness of the algorithm. Combined with an RGB-D camera, the target is reconstructed to obtain the 3-D pose of the target. The tracking and intercepting strategy is adopted to grasp the moving target. The proposed method is proven to have good performance through dataset comparison tests and experiments on real robot systems.

Cu₂ZnSnS₄ (CZTS) has been recognized as a promising thin-film absorber material of chalcopyrite-related solar cells. A two-stage method for fabricating CZTS films using CZTS nanoparticles was developed. Nanocrystal inks fabricated by a ball-milling method was utilized to °C deposit CZTS precursors by spin-coating approach. The CZTS precursors were annealed in the sulfur atmosphere under different annealing temperatures ranging from 550 °C to 650 °C. Influences of annealing temperature on grain growth, composition, crystallinity, and photovoltaic properties of CZTS films were characterized. With the increase of annealing temperature, grain growth was enhanced, while the sulfur atomic ratio fist increased then decreased. The crystallinity of the films was significantly improved after the annealing, and the obvious peak of the secondary phase of ZnS, were observed from the X-ray diffraction results, when the annealing temperature increased to 625 °C. However, the secondary phase was not detected from the surface Raman spectrum. Through comparing the Raman spectrum of different areas of the CZTS film, secondary phases of ZnS and SnS were observed, indicating the decomposition of CZTS films, due to the high temperature. The highest conversion efficiency of 7.5% was obtained when the annealing temperature was 600 °C.

Binary nanoparticle inks comprising Ag₂Se, In₂Se₃, and Ga₂Se₃ were fabricated via a wet ball-milling method and were further used to fabricate AgInGaSe₂ (AIGS) precursors by sequentially spraying the inks onto a Mo-coated substrate. AIGS precursors were annealed under a Se atmosphere for 1 h at 570 °C. Na₂Se thin layers of varying thicknesses (0, 5, 10, and 20 nm) were vacuum-evaporated onto the Mo layer prior to the AIGS precursors being fabricated to investigate the influence on AIGS solar cells. Sodium plays a critical role in improving the material properties and performance of AIGS thin-film solar cells. The grain size of the AIGS films was significantly improved by sodium doping. Secondary ion mass spectroscopy illustrated slight surficial sodium segregation and heavy sodium segregation at the AIGS/Mo interface. Double-graded band profiles were observed in the AIGS films. With the increase in Na₂Se thickness, the basic photovoltaic characteristics of the AIGS solar cells were significantly improved. The highest solar cell conversion efficiency of 6.6% (open-circuit voltage: 775.6 mV, short-circuit current: 15.5 mA/cm², fill factor: 54.9%, area: 0.2 cm²) was obtained when the Na₂Se thickness was 20 nm.

In Ag(In,Ga)Se $_2$ (AIGS) solar cells, a MoSe $_2$ thin layer may provide a critical contact between the AIGS layer and the Mo layer. To test this, AIGS films were deposited on Mo-coated substrates without and with preselenization. With preselenization, the MoSe $_2$ thickness in the AIGS samples ranged between 30 nm and 60 nm, while no MoSe $_2$ layer was observed for the AIGS samples without preselenization. The c-axis of the MoSe $_2$ layer was parallel to that of the Mo layer, with $c$ = 12.5 Å. The presence of MoSe $_2$ in the AIGS films was confirmed by secondary ion mass spectrometry and preselenization exhibited no effect on the promotion of sodium diffusion in the AIGS films. Without preselenization, the AIGS and Mo had a Schottky contact with a calculated barrier height between 0.66 and 0.74 eV, based on the AIGS/Mo current–voltage curves. In contrast, the MoSe $_2$ layer converted the AIGS and Mo contact to an ohmic type. Preselenization increased the solar cell conversion efficiency of AIGS to 9.3% (open-circuit voltage: 957.9 mV, short-circuit current: 18.4 mA/cm $^2$ , and fill factor: 52.9%). Additionally, the quantum efficiency was improved. In the absence of preselenization, the conversion efficiency was 5.2% (open-circuit voltage: 850.6 mV, short-circuit current: 13.1 mA/cm $^2$ , and fill factor: 46.7%).

Traditional involute gear pumps find it difficult to meet the requirements of miniaturization and high performance because of the undercutting, trapped oil, and flow pulsation. To eliminate the phenomenon of trapped oil and reduce flow pulsation in the miniature gear pump, a novel miniature double-circular-arc line gear (MDLG) and its manufacturing method are proposed. Firstly, based on the spatial curve meshing theory, the tooth flank equation of the MDLG is established, and the design method of the MDLG hob is presented. Then, the instantaneous flow rate of the MDLG pump is analyzed by using the swept-area method. Subsequently, a hobbing machining model is built on the VERICUT virtual simulation platform, and machining experiments are conducted on a hobbing machine. Furthermore, the manufactured MDLGs are inspected at a gear measuring center. Finally, an MDLG pump prototype is developed and machined. The measurement results show that the total cumulative pitch deviations of the machined MDLGs are controlled within 32.1 μm, achieving the ISO 8 accuracy grade. The theoretical calculations and experimental results in this article verify the feasibility of the design and processing of MDLG pumps, providing a reference for the development of high-performance miniature gear pumps.

With the advancement of deep learning algorithms, semantic segmentation algorithms for remote sensing images (RSIs) have been widely applied. Segment Anything Model (SAM), as a universal image segmentation model, has been utilized in segmentation tasks. However, SAM’s training on natural images results in a lack of understanding of the general information specific to RSIs. Mamba, as a sequence modeling method grounded in the state space model, can directly capture the global dependencies inherent in RSIs. Its integration with SAM significantly enhances segmentation accuracy in complex scenes. Therefore, SFE-SAM is proposed to enhance Mobile SAM’s capability to comprehend complex scenes and capture global contextual information by introducing Mamba. In SFE-SAM, the SSM Tokening Adapter (STA) is employed to fine-tune the extractor of Mobile SAM, establishing long-range feature correlations. Meanwhile, the Dual Feature Enhancement Module (DFEM) is designed to fuse cross-dimensional features. Finally, the Semantic Information Enhance Module (SIEM) is utilized to improve multi-scale information extraction and dynamic feature enhancement. Experimental results demonstrate that SFE-SAM achieves superior segmentation performance compared to SOTA methods on the LandCover.ai and Vaihingen datasets.