Xiang Peng

High-performance all-solid-state supercapacitors (SCs) are fabricated based on thin, lightweight, and flexible freestanding MVNN/CNT hybrid electrodes. The device shows a high volume capacitance of 7.9 F/cm3, volume energy and power density of 0.54 mWh/cm3 and 0.4 W/cm3 at a current density of 0.025 A/cm3. By being highly flexible, environmentally friendly, and easily connectable in series and parallel, the all-solid-state SCs promise potential applications in portable/wearable electronics. As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re-organized for online delivery, but are not copy-edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Recent advances in transition metal nitride-based catalysts for efficient electrochemical water splitting have been reviewed.

Abstract Polymer binders with high ion and electron conductivities are prepared by assembling ionic polymers (polyethylene oxide and polyethylenimine) onto the electrically conducting polymer poly(3,4‐ethylenedioxythiophene): poly(styrenesulfonate) chains. Crosslinking, chemical reductions, and electrostatics increase the modulus of the binders and maintain the integrity of the anode. The polymer binder shows lithium‐ion diffusivity and electron conductivity that are 14 and 90 times higher than those of the widely used carboxymethyl cellulose (with acetylene black) binder, respectively. The silicon anode with the polymer binder has a high reversible capacity of over 2000 mA h g −1 after 500 cycles at a current density of 1.0 A g −1 and maintains a superior capacity of 1500 mA h g −1 at a high current density of 8.0 A g −1 .

Point cloud completion aims to predict a complete shape in high accuracy from its partial observation. However, previous methods usually suffered from discrete nature of point cloud and unstructured prediction of points in local regions, which makes it hard to reveal fine local geometric details on the complete shape. To resolve this issue, we propose SnowflakeNet with Snowflake Point Deconvolution (SPD) to generate the complete point clouds. The SnowflakeNet models the generation of complete point clouds as the snowflake-like growth of points in 3D space, where the child points are progressively generated by splitting their parent points after each SPD. Our insight of revealing detailed geometry is to introduce skip-transformer in SPD to learn point splitting patterns which can fit local regions the best. Skip-transformer leverages attention mechanism to summarize the splitting patterns used in the previous SPD layer to produce the splitting in the current SPD layer. The locally compact and structured point cloud generated by SPD is able to precisely capture the structure characteristic of 3D shape in local patches, which enables the network to predict highly detailed geometries, such as smooth regions, sharp edges and corners. Our experimental results outperform the state-of-the-art point cloud completion methods under widely used benchmarks. Code will be available at https://github.com/AllenXiangX/SnowflakeNet.