Lithium‐sulfur battery as one of the most promising and attractive candidate among the emerging electrical energy storage has attracted enormous attentions. It has superior characteristics of high specific energy density (2600 Wh kg−1) and high theoretical specific capacity (1675 mAh g−1), which is equal to 3–5 times of lithium‐ion batteries, and more closed to the requirement of the pure electric vehicles and hybrid electric vehicles. Furthermore, sulfur element is inexpensive, naturally abundant, and environmentally friendly. However, the commercial application of lithium‐sulfur batteries (LSBs) still faces some major technical obstacles such as the low electrical conductivity of sulfur, the shuttle effect of polysulfides, and the drastic volume expansion during charge/discharge process. In this review paper, we focus on some of the effective strategies in boosting the electrochemical performance of LSBs through the development of sulfur/carbon composite electrode materials, including the use of porous carbons, carbon nanotubes/fibers, and graphene. The integration of carbon materials and sulfur can efficiently improve the utilization of active materials, enhance the conductivity of cathode materials, and provide a polysulfides barrier. Simultaneously, the challenges and prospects on LSBs in the near future are also presented and discussed.

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Metallic zinc (Zn) is one of the most attractive multivalent-metal anode materials in post-lithium batteries because of its high abundance, low cost and high theoretical capacity. However, it usually suffers from large voltage polarization, low Coulombic efficiency and high propensity for dendritic failure during Zn stripping/plating, hindering the practical application in aqueous rechargeable zinc-metal batteries (AR-ZMBs). Here we demonstrate that anionic surfactant-assisted in situ surface alloying of Cu and Zn remarkably improves Zn reversibility of 3D nanoporous Zn electrodes for potential use as high-performance AR-ZMB anode materials. As a result of the zincophilic ZnxCuy alloy shell guiding uniform Zn deposition with a zero nucleation overpotential and facilitating Zn stripping via the ZnxCuy/Zn galvanic couples, the self-supported nanoporous ZnxCuy/Zn electrodes exhibit superior dendrite-free Zn stripping/plating behaviors in ambient aqueous electrolyte, with ultralow polarizations under current densities up to 50 mA cm‒2, exceptional stability for 1900 h and high Zn utilization. This enables AR-ZMB full cells constructed with nanoporous ZnxCuy/Zn anode and KzMnO2 cathode to achieve specific energy of as high as ~ 430 Wh kg‒1 with ~ 99.8% Coulombic efficiency, and retain ~ 86% after long-term cycles for > 700 h.

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A novel NiO/Ni/RGO three-dimensional core-shell architecture consisting of Ni nanoparticles as core, NiO as shell and reduced graphene oxide (RGO) as conductivity layer, has been constructed by redox reactions with hydrothermal method and heat treatment. High density arrayed nickel nanoparticles (20 nm diameter) semi-coated by a 3 nm thick layer of NiO are evenly distributed on the surface of graphene. This elaborate design not only uses abundant NiO surfaces to provide a wealth of active sites, but also bridges electrochemical active NiO shell and graphene by Ni core to construct an interconnected 3D conductive network. Since both electrochemical activity and excellent conductivity are reserved in this Ni/NiO core-shell/graphene layer 3D structure, the as-prepared electrode material exhibits an extremely high specific capacitance (2048.3 F g−1 at current density of 1 A g−1) and excellent cycle stability (77.8% capacitance retention after 10000 cycles at current density of 50 A g−1). The novel method presented here is easy and effective and would provide reference for the preparation of other high performance supercapacitor electrodes.

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In recent years, support vector machine (SVM) has been widely applied in remote sensing image classification, since its experience can also minimize errors and maximize the geometric characteristics of the edge area. In this article SVM classification algorithm will be introduced the remote sensing extraction coastline. Fujian Province Landsat7 ETM + image will be a test region to be classified the image and extract the shoreline. Then based on the coastline formula calculate modified the shoreline in the ArcGIS and completed the extraction of coastline