Bifunctional Square-Planar NiO4 Coordination of Topotactic LaNiO2.0 Films for Efficient Oxygen Evolution Reaction.

The high-efficient and low-cost oxygen evolution reaction (OER) is decisive for applications of oxide catalysts in metal-air batteries, electrolytic cells, and energy-storage technologies. Delicate regulations of active surface and catalytic reaction pathway of oxide materials principally determine thermodynamic energy barrier and kinetic rate during catalytic reactions, and thus have crucial impacts on OER performance. Herein, a synergistic modulation of catalytically active surface and reaction pathway through facile topotactic transformations switching from perovskite (PV) LaNiO3.0 film to infinite-layer (IL) LaNiO2.0 film is demonstrated, which absolutely contributes to improving OER performance. The square-planar NiO4 coordination of IL-LaNiO2.0 brings about more electrochemically active metal (Ni+ ) sites on the film surface. Meanwhile, the oxygen-deficient driven PV- IL topotactic transformations lead to a reaction pathway converted from absorbate evolution mechanism to lattice-oxygen-mediated mechanism (LOM). The non-concerted proton-electron transfer of LOM pathway, evidenced by the pH-dependent OER kinetics, further boosts the OER activity of IL-LaNiO2.0 films. These findings will advance the in-depth understanding of catalytic mechanisms and open new possibilities for developing highly active perovskite-derived oxide catalysts.

Application of Machine Vision System in Food Detection.

Food processing technology is an important part of modern life globally and will undoubtedly play an increasingly significant role in future development of industry. Food quality and safety are societal concerns, and food health is one of the most important aspects of food processing. However, ensuring food quality and safety is a complex process that necessitates huge investments in labor. Currently, machine vision system based image analysis is widely used in the food industry to monitor food quality, greatly assisting researchers and industry in improving food inspection efficiency. Meanwhile, the use of deep learning in machine vision has significantly improved food identification intelligence. This paper reviews the application of machine vision in food detection from the hardware and software of machine vision systems, introduces the current state of research on various forms of machine vision, and provides an outlook on the challenges that machine vision system faces.