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Highlights Ordered intermetallic pallium-zinc (PdZn) electrocatalyst comprising a high density of PdZn pairs is synthesized for achieving co-adsorption and co-activation carbon dioxide and nitrate. Both operando measurements and theoretical calculations reveal that the PdZn pairs provide a dual-site geometric structure conducive to the key C–N coupling with a low kinetical barrier. The intermetallic PdZn displays excellent performance for the co-reduction of nitrate (NO3 –) and carbon dioxide (CO2) toward urea, with a maximum FEurea of 62.78% at a small potential of – 0.4 V versus RHE.

Abstract Designing dense thick sulfur cathodes to gain high‐volumetric/areal‐capacity lithium–sulfur batteries (LSBs) in lean electrolytes is extremely desired. Nevertheless, the severe Li2S clogging and unclear mechanism seriously hinder its development. Herein, an integrated strategy is developed to manipulate Li2S redox kinetics of CoP/MXene catalyst via electron‐donor Cu doping. Meanwhile a dense S/Cu0.1Co0.9P/MXene cathode (density = 1.95 g cm−3) is constructed, which presents a large volumetric capacity of 1664 Ah L−1 (routine electrolyte) and a high areal capacity of ≈8.3 mAh cm−2 (lean electrolyte of 5.0 µL mgs−1) at 0.1 C. Systematical thermodynamics, kinetics, and theoretical simulation confirm that electron‐donor Cu doping induces the charge accumulation of Co atoms to form more chemical bonding with polysulfides, whereas weakens CoS bonding energy and generates abundant lattice vacancies and active sites to facilitate the diffusion and catalysis of polysulfides/Li2S on electrocatalyst surface, thereby decreasing the diffusion energy barrier and activation energy of Li2S nucleation and dissolution, boosting Li2S redox kinetics, and inhibiting shuttling in the dense thick sulfur cathode. This work deeply understands the atomic‐level manipulation mechanism of Li2S redox kinetics and provides dependable principles for designing high‐volumetric‐energy‐density, lean‐electrolyte LSBs through integrating bidirectional electro‐catalysts with manipulated Li2S redox and dense‐sulfur engineering.

Euodia ruticarpa is a medicinal plant recorded in in Chinese Pharmacopeia. Here we report on the complete chloroplast genome sequence of Euodia ruticarpa. The chloroplast genome is 158,762 bp in size and includes two inverted repeat regions of 54,230 bp, which is separated by a large single-copy region of 86,267 bp and a small single copy region of 18,265 bp. A total of 131 genes were predicted, including 37 tRNA, 8 rRNA, and 86 protein-coding genes. Phylogenetic analysis placed Euodia ruticarpa under the family Rutaceae.

MoO3 nanobelts were prepared by a one-step hydrothermal method and then assembled with aluminum nanoparticles using polyvinylpyrrolidone as a binder. The physicochemical properties of the as-prepared samples were carefully characterized by scanning electron microscopy, transmission electron microscopy, X-ray diffraction, thermogravimetric analysis/differential scanning calorimetry, and drop weight impact test. The results showed that the Tonset of Mo-Al-0, Mo-Al-1, Mo-Al-2, and Fe-Al-0 are 474.8°C, 484.2°C, 478.5°C, and 514.8°C, respectively, which are 66.0°C, 56.6°C, 62.3°C, and 26.0°C lower than that of pure Al-NPs (540.8°C). The total exothermic heat of selfassembled MoO3/Al during DSC test is about 2626.9 J/g, which is 229.6, 420.8, and 11.1 J/g higher than that of Al/commercial MoO3, Al/hydrothermal MoO3, and Al/referenced Fe2O3. Furthermore, the high exothermic self-assembled MoO3/Al thermite is totally insensitive to impact. [ABSTRACT FROM AUTHOR]

Dense flat membranes of sulfonated polyimides (SPIs) were prepared from 1,4,5,8-naphthalene tetracarboxylic dianhydride (NTDA) and aromatic diamines of 2,2-bis[4-(4-aminophenoxy)phenyl]hexafluoropropane disulfonic acid, benzidine-2,2′-disulfonic acid, 4,4′-diaminodiphenylether-3,3′-disulfonic acid and 9,9′-bis(4-aminophenyl)fluorene. The SPI membranes were pyrolyzed at a relatively low-temperature of 450 °C for 1.5 h at a nitrogen flow. During the pyrolysis, the —SO3H groups decomposed without substantial cleavage of the polyimide backbone. The pyrolytic membranes had the interesting intermixed properties of toughness and good flexibility as in polymer membranes and high gas permeability with reasonably high selectivity as in carbon molecular sieve membranes. The gas permeability for the pyrolytic membranes seems to be controlled by the sulfonic acid group content in the precursor polyimides, the relaxation of the polymer chains during the pyrolysis and the gas permeation level of the precursor membranes. Decomposition of —SO3H groups induced microvoids, which are considered to remain to some extent as larger-size free volume holes in the polymer matrix, resulting in the higher diffusivity to gas with larger diameter. The pyrolytic membranes showed the higher permeability of C3H6 and/or the higher selectivity of C3H6/C3H8 than the 6FDA-polyimide membranes which have defined the upper bound line. [Copyright &y& Elsevier]

Using machine vision to identify and sort scattered regular targets is an urgent problem to be solved in automated production lines. This study proposed a three-dimensional (3D) recognition method combining monocular vision and machine learning algorithms. According to the color characteristics of the targets, to convert the original color picture into YCbCr mode and use the 2D Otsu algorithm to perform gray level image segmentation on the Cb channel. Then the Haar-feature training was carried out. The comparison of feature training and Haar method for Hough transform showed that the recognized time of Haar-feature AdaBoost trainer reached 31.00 ms, while its false recognized rate was 3.91%. The strong classifier was formed by weight combination, and the Hough contour transformation algorithm was set to correct the normal vector between plane coordinate and camera coordinate system. The monocular vision system ensured that the field of camera view had not obstructed while the dots were being struck. It was measured and calculated angles between targets and the horizontal plane which coordinate points of the identified plane feature. The testing results were compared with the Otsu and AdaBoost trainer where the prediction and training set have an error of no more than 0.25 mm. Its correct rate can reach 95%. It shows that the Otsu and Haar-feature based on AdaBoost algorithm is feasible within a certain error ranges and meet the engineering requirements for solving the poses of automated regular three-dimensional targets. [ABSTRACT FROM AUTHOR]