Electrochemical Reducation of TiO2/Al2O3/C to Ti3AlC2 and Its Derived Two-Dimensional (2D) Carbides

Ti3AlC2 has been directly synthesized from TiO2/Al2O3/C mixture precursors (3TiO2/0.5Al2O3/1.5C and 2TiO2/0.5Al2O3/C) by a molten salt electrolysis process at 900?C and 3.2 V in molten CaCl2. The influence of initial carbon content on the electrosynthesized products has been investigated. The result shows that the main phase of the electrosynthesized products changes from Ti3AlC to Ti2AlC and then to Ti3AlC2 with the increasing carbon content, and the electrosynthesized Ti3AlC2 is carbon deficient. The morphology observation shows that the electrosynthesized Ti3AlC2 particles possess smooth surfaces and dense flake-like microstructure. The reaction mechanism of the electroreduction of TiO2/Al2O3/C mixture precursor has been discussed based on the time- and position-dependent phase constitution analysis. In addition, two-dimensional (2D) Ti3AlC2-derived carbides, i.e., Ti3C2Tx and TiCx have been successfully prepared from the electrosynthesized Ti3AlC2 by a chemical etching process and an electrochemical etching process, respectively. Both derived carbides exhibit the similar layered structure, in which single layer carbides are composed of plentiful nanometer carbides. It is suggested that the molten salt electrolysis process has a great potential to be used for the facile synthesis of Mn+1AXn phases (such as Ti3AlC2) from their oxides precursors, and the synthesized Mn+1AXn phases can be further converted into 2D carbidesauthorsversionPeer reviewe

High-performance ZnIn₂S₄/Ni(dmgH)₂ for photocatalytic hydrogen evolution: ion exchange construction, photocorrosion mitigation, and efficiency enhancement by photochromic effect

Abstract In this work, a novel photocatalyst of ZnIn₂S₄/Ni(dmgH)₂ was designed by a simple chemical precipitation method and used to enhance hydrogen evolution under visible light irradiation. Along with vigorous discharges of hydrogen bubbles, an optimal rate of 36.3 mmol/g/h was reached under UV–Vis light for hydrogen evolution, nearly 4.9 times of the one from pure ZnIn₂S₄. The heterojunction exhibits steady hydrogen evolution capability and owns a high apparent quantum efficiency (AQE) of 20.45% under the monochromatic light at 420 nm. By coupling ZnIn₂S₄ with Ni(dmgH)₂, an extraordinary photochromic phenomenon was detected and attributed to the active Ni-S component in situ formed between the nickel and sulfur composites under light irradiation. The emerging sulfide benefits light absorption of the system and separation of photogenerated electron and hole pairs. Besides providing a promising photocatalyst for visible light hydrogen production, the present work is hoped to inspire new trends of catalytic medium designs and investigations

Review: Deep Learning on 3D Point Clouds

A point cloud is a set of points defined in a 3D metric space. Point clouds have become one of the most significant data formats for 3D representation and are gaining increased popularity as a result of the increased availability of acquisition devices, as well as seeing increased application in areas such as robotics, autonomous driving, and augmented and virtual reality. Deep learning is now the most powerful tool for data processing in computer vision and is becoming the most preferred technique for tasks such as classification, segmentation, and detection. While deep learning techniques are mainly applied to data with a structured grid, the point cloud, on the other hand, is unstructured. The unstructuredness of point clouds makes the use of deep learning for its direct processing very challenging. This paper contains a review of the recent state-of-the-art deep learning techniques, mainly focusing on raw point cloud data. The initial work on deep learning directly with raw point cloud data did not model local regions; therefore, subsequent approaches model local regions through sampling and grouping. More recently, several approaches have been proposed that not only model the local regions but also explore the correlation between points in the local regions. From the survey, we conclude that approaches that model local regions and take into account the correlation between points in the local regions perform better. Contrary to existing reviews, this paper provides a general structure for learning with raw point clouds, and various methods were compared based on the general structure. This work also introduces the popular 3D point cloud benchmark datasets and discusses the application of deep learning in popular 3D vision tasks, including classification, segmentation, and detection

Deep regression for LiDAR-based localization in dense urban areas

LiDAR-based localization in a city-scale map is a fundamental question in autonomous driving research. As a reasonable localization scheme, the localization can be performed by global retrieval (that suggests potential candidates from the database) followed by geometric registration (that obtains an accurate relative pose). In this work, we develop a novel end-to-end, deep multi-task network that simultaneously performs global retrieval and geometric registration for LiDAR-based localization. Both retrieval and registration are formulated and solved as regression problems, and they can be deployed independently during inference time. We also design two mechanisms to enhance our multi-task regression network\u27s performance: residual connections for point clouds and a new loss function with learnable parameters. To alleviate the common phenomenon of vanishing gradients in neural networks, we employ residual connections to support constructing a deeper network effectively. At the same time, to solve the problem of huge differences in scale and units between different tasks, we propose a loss function that can automatically balance multi-tasks. Experiments on two public benchmarks validate the state-of-the-art performance of our algorithm in large-scale LiDAR-based localization

Facilitation of Rice Stripe Virus Accumulation in the Insect Vector by Himetobi P Virus VP1

The small brown planthopper (SBPH) is the main vector for rice stripe virus (RSV), which causes serious rice stripe disease in East Asia. To characterize the virus-vector interactions, the SBPH cDNA library was screened with RSV ribonucleoprotein (RNP) as bait using a GAL4-based yeast two-hybrid system. The interaction between RSV-RNP and the Himetobi P virus (HiPV, an insect picorna-like virus) VP1 protein was identified. The relationships between HiPV and RSV in SBPH were further investigated, and the results showed that the titer of RSV was commonly higher in single insect that exhibited more VP1 expression. After the VP1 gene was repressed by RNA silencing, the accumulation of RSV decreased significantly in the insect, whereas the virus acquisition ability of SBPH was unaffected, which suggests that HiPV VP1 potentially facilitates the accumulation of RSV in SBPH

Improving the estimation of canopy cover from UAV-LiDAR data using a pit-free CHM-based method

Accurate and rapid estimation of canopy cover (CC) is crucial for many ecological and environmental models and for forest management. Unmanned aerial vehicle-light detecting and ranging (UAV-LiDAR) systems represent a promising tool for CC estimation due to their high mobility, low cost, and high point density. However, the CC values from UAV-LiDAR point clouds may be underestimated due to the presence of large quantities of within-crown gaps. To alleviate the negative effects of within-crown gaps, we proposed a pit-free CHM-based method for estimating CC, in which a cloth simulation method was used to fill the within-crown gaps. To evaluate the effect of CC values and within-crown gap proportions on the proposed method, the performance of the proposed method was tested on 18 samples with different CC values (40−70%) and 6 samples with different within-crown gap proportions (10−60%). The results showed that the CC accuracy of the proposed method was higher than that of the method without filling within-crown gaps (R2 = 0.99 vs 0.98; RMSE = 1.49% vs 2.2%). The proposed method was insensitive to within-crown gap proportions, although the CC accuracy decreased slightly with the increase in within-crown gap proportions