Controlled three-dimensional leaf-like NiCoO2@NiCo layered double hydroxide heterostructures for oxygen evolution electrocatalysts in rechargeable Zn-air batteries.

Rechargeable zinc-air batteries (ZABs) have garnered attention as a viable choice for large-scale energy storage due to their advantageous characteristics, such as high energy density and cost-effectiveness. Strategies aimed at improving the kinetics of the oxygen evolution reaction (OER) through advanced electrocatalytic materials or structural designs can significantly enhance the efficiency and longevity of ZABs. In this study, we introduce a three-dimensional (3D) leaf-vein system heterojunction architecture. In this structure, NiCoO2 nanowire arrays form the central vein, surrounded by an outer leaf composed of NiCo layered double hydroxide (LDH) nanosheets. All these components are integrated onto a substrate made of Ni foam. Notably, when tested in an alkaline environment, the NiCoO2@NiCo LDH exhibited an overpotential of 272 mV at a current density of 10 mA cm-2, and extended durability evaluations over 12 h underscored its robustness at 99.76 %. The rechargeable ZABs achieved a peak power density of 149 mW cm-2. Furthermore, the NiCoO2@NiCo LDH demonstrated stability by maintaining high round-trip efficiencies throughout more than 680 cycles (equivalent to 340 h) under galvanostatic charge-discharge cycling at 5 mA cm-2. The leaf-vein system heterojunction significantly increased the active sites of the catalysts, facilitating charge transport, improving electronic conductivity, and enhancing overall stability.

Remote SERS detection at a 10-m scale using silica fiber SERS probes coupled with a convolutional neural network.

A silica fiber surface-enhanced Raman scattering (SERS) probe provides a practical way for remote SERS detection of analytes, but it faces the major bottleneck that the relatively large Raman background of silica fiber itself greatly limits the remote detection sensitivity and distance. In this article, we developed a convolutional neural network (CNN)-based deep learning algorithm to effectively remove the Raman background of silica fiber itself and thus significantly improved the remote detection capability of the silica fiber SERS probes. The CNN model was constructed based on a U-Net architecture and instead of concatenating, the residual connection was adopted to fully leverage the features of both the shallow and deep layers. After training, this CNN model presented an excellent background removal capacity and thus improved the detection sensitivity by an order of magnitude compared with the conventional reference spectrum method (RSM). By combining the CNN algorithm and the highly sensitive fiber SERS probes fabricated by the laser-induced evaporation self-assembly method, a limit of detection (LOD) as low as 10-8 M for Rh6G solution was achieved with a long detection distance of 10 m. To the best of our knowledge, this is the first report of remote SERS detection at a 10-m scale with fiber SERS probes. As the proposed remote detection system with silica fiber SERS probes was very simple and low cost, this work may find important applications in hazardous detection, contaminant monitoring, and other remote spectroscopic detection in biomedicine and environmental sciences.