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High-specific-surface-area ceramic catalyst carriers with hierarchical pores are conducive to catalytic reactions and filtration processes in the chemical engineering industry. However, conventional processes have difficulty manufacturing porous ceramics with extremely intricate structures. Herein, a novel method was proposed to fabricate alumina ceramic catalyst carriers with a high specific surface area (SSA) and hierarchical porosity by powder bed fusion (PBF) using micromesoporous γ-alumina as a pore-forming powder and an impregnation process of colloidal silica. Hierarchical porosity adjustable on the nm (6.03–32.37 nm), μm (24–30 μm), and mm (0.5~4.4 mm) scales was obtained by adopting and adjusting the PBF, impregnation process on preforms and postsintering process. The impacts of SiO2 content and post sintering temperatures on mechanical properties, pore characteristics, and SSA have been scientifically researched. The PBF-formed alumina ceramics possessed a crush strength of 45.88 ± 3.79 N/cm, high SSA of 56.324 m2/g, and porosity of 72.88 ± 3.37 % with multimodal distributions at 12.24 nm, 32.37 nm, and 24.18 μm. The improvement of SSA was mainly attributed to the use of micromesoporous γ-alumina and an impregnation process to reduce 3D-printing defects to obtain the hierarchical porosity (nm ~ μm scales) and required strength at a lower sintering temperature. Monolithic catalyst carriers with controllable porous structures have been manufactured to facilitate industrial recycling and improve efficiency.

Lunar regolith plays an extremely important role in the establishment of permanent human residence or robotic industries on the Moon. Lunar regolith environment construction may accelerate the progress of lunar engineering, biology, medicine, and agriculture for future colonization of the Moon. Although advances in investigating and simulating lunar regolith have achieved unprecedented progress, producing simulated lunar regolith environment on Earth with adequate fidelity remains challenging. Inspired by traditional Chinese tofu making process, we proposed a comprehensive methodology to construct lunar regolith environment on Earth, and developed theoretical models to illustrate gas and heat transfer behavior in this particulate and porous material. Our study, proven to be effective, reveals a complex but generic gas and heat transfer mechanism in granular lunar regolith simulant, and can be readily applied to construct experimental regolith environments for lunar scientific research.

Most welding manufacturing of the heavy industry, such as shipbuilding and construction, is carried out in an unstructured workspace. The term Unstructured indicates the production environment is irregular, changeable and without model. In this case, the changeable workpiece position, workpiece shape, environmental background, and environmental illumination should be carefully considered. Because of such complicated characteristics, the welding is currently being relied on the manual operation, resulting in high cost, low efficiency and quality. This work proposes a portable robotic welding system and a novel seam tracking method. Compared to existing methods, it can cope with more complex general spatial curve weld. Firstly, the tracking pose of the robot is modeled by a proposed dual-sequence tracking strategy. On this basis, the working parameters can be adjusted to avoid robot-workpiece collision around the workpiece corners during the tracking process. By associating the forward direction of the welding torch with the viewpoint direction of the camera, it solves the problem that the weld feature points are prone to be lost in the tracking process by conventional methods. Point cloud registration is adopted to globally locate the multi-segment welds in the workpiece, since the system deployment location is not fixed. Various experiments on single or multiple welds under different environmental conditions show that even if the robot is deployed in different positions, it can reach the starting point of the weld smoothly and accurately track along the welds.

Nano zero-valent iron (nZVI) materials usually have low selectivity in water toward target contaminants (e.g., p-nitrophenol) and nontarget water constituents (e.g., nitrates and dissolved organic matter-DOM). Here we prepared the selective nZVI entrapped in the matrix of cyclodextrin polymer (CDP), using in situ impregnation and aqueous KBH4 reduction of FeCl3 in the matrix of CDP. The nZVI-CDP composites feature the network structure of CDP with mainly 40–80 nm nZVI particles entrapped in the network and 300 nm iron particles coated on the network surface. nZVI entrapped displays high stability and selective CDP facilitates the access of p-nitrophenol to nZVI, both accounting for the high selectivity of the composites. Chemical degradation of the nZVI materials, combined with adsorption, contributed to the p-nitrophenol removal. The nZVI materials usually exhibited similar or higher p-nitrophenol removal at different levels of nitrates, DOM or sulfides. Column experiments showed that the nZVI materials completely decontaminated 63 column volumes of groundwater containing p-nitrophenol, 6-fold higher than mixed nZVI and CDP. The nZVI materials were estimated to have potential performance duration of averaging 109 d, indicating the long-term performance in groundwater.

Camera calibration is a fundamental task in photogrammetry and computer vision. In view of the requirements in live camera characteristics, we present a novel calibration approach to obtain all the camera optimal parameters and the distortion rectification by using only a single image. The existing automatic calibration approaches inspired by the vanishing point theory or the homography matrix are not capable of entirely deal with the internal, external and the len distortion parameters simultaneously. Compared to the previous works, our approach solves the problem by applying the linear features extracted from the image and thus improves the accuracy and efficiency. Results of experiments in different scenes demonstrate the comparable performance to the traditional methods.

In this paper, a robotic seam tracking system with welding posture estimation is proposed that can adapt to different conditions encountered in the field of welding, such as uncertain working positions and complex workpiece shapes. In the proposed system, the target coordinate system of the welding torch is established in real-time at each welding position, and the rotation angles are obtained to change the welding posture of the robot. Gaussian kernel correlation filter is used to track the weld feature in real-time that improves the accuracy and robustness of welding seam tracking. Compared with morphological methods, this method can quickly and accurately find the position of the weld from the noisy image. Finally, experimental results show that the method can be used to calculate the welding posture, which meet the welding requirements of a complex environment.