"Surface-Like Growth" Strategy for the Direct Synthesis of Horizontally Aligned Boron Nitride Nanotubes.

The inherent properties of boron nitride nanotubes (BNNTs) can be further enhanced through the control of their anisotropy. In particular, horizontally aligned BNNTs (HABNNTs) exhibit considerable potential for various applications. However, directly synthesizing HABNNTs is difficult owing to the random floating of BNNTs and the absence of directional forces. Here, we employed a simple, efficient, and universal "surface-like growth" strategy to synthesize high-density and high-quality HABNNTs in the W2B5/Zn precursor system. First, the floating range of BNNTs was restricted to the vicinity of the precursor, and then, directional forces were applied to induce BNNT directional growth along the substrate surface. Experiments and simulations confirmed that the HABNNT orientation could be controlled through manipulation of the directional forces. Furthermore, the strategy was employed for HABNNTs synthesis using the MoB2/Zn, further demonstrating the universality of the approach. Overall, this work offers a fresh perspective on the synthesis of HABNNTs, further expanding their potential applications.

Direct measurement of built-in electric field inside a 2D cavity.

The on-demand assembly of 2D heterostructures has brought about both novel interfacial physical chemistry and optoelectronic applications; however, existing studies rarely focus on the complementary part-the 2D cavity, which is a new-born area with unprecedented opportunities. In this study, we have investigated the electric field inside a spacer-free 2D cavity consisting of a monolayer semiconductor and a gold film substrate. We have directly captured the built-in electric field crossing a blinking 2D cavity using a Kelvin probe force microscopy-Raman system. The simultaneously recorded morphology (M), electric field (E), and optical spectroscopy (O) mapping profile unambiguously reveals dynamical fluctuations of the interfacial electric field under a constant cavity height. Moreover, we have also prepared non-blinking 2D cavities and analyzed the gap-dependent electric field evolution with a gradual heating procedure, which further enhances the maximum electric field exceeding 109 V/m. Our work has revealed substantial insights into the built-in electric field within a 2D cavity, which will benefit adventures in electric-field-dependent interfacial sciences and future applications of 2D chemical nanoreactors.

Molecular Engineering Enables Hydrogel Electrolyte with Ionic Hopping Migration and Self-Healability toward Dendrite-Free Zinc-Metal Anodes.

Hydrogel electrolytes (HEs), characterized by intrinsic safety, mechanical stability, and biocompatibility, can promote the development of flexible aqueous zinc-ion batteries (FAZIBs). However, current FAZIB technology is severely restricted by the uncontrollable dendrite growth arising from undesirable reactions between the HEs with sluggish ionic conductivity and Zn metal. To overcome this challenge, this work proposes a molecular engineering strategy, which involves the introduction of oxygen-rich poly(urea-urethane) (OR-PUU) into polyacrylamide (PAM)-based HEs. The OR-PUU/PAM HEs facilitate rapid ion transfer through their ionic hopping migration mechanism, resulting in uniform and orderly Zn2+ deposition. The abundant polar groups on the OR-PUU molecules in OR-PUU/PAM HEs break the inherent H-bond network, tune the solvation structure of hydrated Zn2+, and inhibit the occurrence of side reactions. Moreover, the interaction of hierarchical H-bonds in the OR-PUU/PAM HEs endows them with self-healability, enabling in situ repair of cracks induced by plating/stripping. Consequently, Zn symmetric cells incorporating the novel OR-PUU/PAM HEs exhibit a long cycling life of 2000 h. The resulting Zn-MnO2 battery displays a low capacity decay rate of 0.009% over 2000 cycles at 2000 mA g-1. Overall, this work provides valuable insights to facilitate the realization of dendrite-free Zn-metal anodes through the molecular engineering of HEs.

Organic molecule-assisted intermediate adsorption for conversion of CO2 to CO by electrocatalysis.

Currently, Zn-based catalysts for electrochemical CO2 reduction reactions are limited by their moderate carbophilicity, resulting in low catalytic activity and CO selectivity. To this end, we selected 5-mercapto-1-methylimidazole, a small molecule that possesses the ability to both coordinate to Zn and interact with the intermediates, to modify electrochemically deposited Zn nanosheets. The interaction between them effectively enhances intermediate adsorption by lowering the Gibbs free energy, which leads to an increase of the Faraday efficiency to 1.9 times and the CO partial current density to 3.0 times that of the pristine sample (-1.0 V vs. RHE).

Self-Catalytic Ternary Compounds for Efficient Synthesis of High-Quality Boron Nitride Nanotubes.

The large-scale synthesis of high-quality boron nitride nanotubes (BNNTs) has attracted considerable interests due to their applications in nanocomposites, thermal management, and so on. Despite decades of development, efficient preparation of high-quality BNNTs, which relies on the effective design of precursors and catalysts and deep insights into the catalytic mechanisms, is still urgently needed. Here, a self-catalytic process is designed to grow high-quality BNNTs using ternary W-B-Li compounds. W-B-Li compounds provide boron source and catalyst for BNNTs growth. High-quality BNNTs are successfully obtained via this approach. Density functional theory-based molecular dynamics (DFT-MD) simulations demonstrate that the Li intercalation into the lattice of W2 B5 promotes the formation of W-B-Li liquid and facilitates the compound evaporation for efficient BNNTs growth. This work demonstrates a high-efficient self-catalytic growth of high-quality BNNTs via ternary W-B-Li compounds, providing a new understanding of high-quality BNNTs growth.

Pixel2Mesh: 3D Mesh Model Generation via Image Guided Deformation.

In this paper, we propose an end-to-end deep learning architecture that generates 3D triangular meshes from single color images. Restricted by the nature of prevalent deep learning techniques, the majority of previous works represent 3D shapes in volumes or point clouds. However, it is non-trivial to convert these representations to compact and ready-to-use mesh models. Unlike the existing methods, our network represents 3D shapes in meshes, which are essentially graphs and well suited for graph-based convolutional neural networks. Leveraging perceptual features extracted from an input image, our network produces the correct geometry by progressively deforming an ellipsoid. To make the whole deformation procedure stable, we adopt a coarse-to-fine strategy, and define various mesh/surface related losses to capture properties of various aspects, which benefits producing the visually appealing and physically accurate 3D geometry. In addition, our model by nature can be adapted to objects in specific domains, e.g., human faces, and be easily extended to learn per-vertex properties, e.g., color. Extensive experiments show that our method not only qualitatively produces the mesh model with better details, but also achieves the higher 3D shape estimation accuracy compared against the state-of-the-arts.