Noncentrosymmetric Tetrel Pnictides RuSi4P4 and IrSi3P3: Nonlinear Optical Materials with Outstanding Laser Damage Threshold

Noncentrosymmetric (NCS) tetrel pnictides have recently generated interest as nonlinear optical (NLO) materials due to their second harmonic generation (SHG) activity and large laser damage threshold (LDT). Herein nonmetal-rich silicon phosphides RuSi₄P₄ and IrSi₃P₃ are synthesized and characterized. Their crystal structures are reinvestigated using single crystal X-ray diffraction and ²⁹Si and ³¹P magic angle spinning NMR. In agreement with previous report RuSi₄P₄ crystallizes in NCS space group P1, while IrSi₃P₃ is found to crystallize in NCS space group Cm, in contrast with the previously reported space group C2. A combination of DFT calculations and diffuse reflectance measurements reveals RuSi₄P₄ and IrSi₃P₃ to be wide bandgap (E_g) semiconductors, E_g = 1.9 and 1.8 eV, respectively. RuSi₄P₄ and IrSi₃P₃ outperform the current state-of-the-art infrared SHG material, AgGaS₂, both in SHG activity and laser inducer damage threshold. Due to the combination of high thermal stabilities (up to 1373 K), wide bandgaps (≈2 eV), NCS crystal structures, strong SHG responses, and large LDT values, RuSi₄P₄ and IrSi₃P₃ are promising candidates for longer wavelength NLO materials.     

浅谈电子电工技术在电力系统中的应用

本文针对电子电工技术在电力系统中的作用进行了分析,同时为该技术的推广提出了相应的建议。     

基于阶段注意力机制的电力线提取算法

电力系统维护是电力系统稳定运行的重要保障，应用智能算法的无人机电力巡检则为电力系统维护提供便捷。电力线提取是自主电力巡检以及保障飞行器低空飞行安全的关键技术，结合深度学习理论进行电力线提取是电力巡检的重要突破点。本文将深度学习方法用于电力线提取任务，结合电力线图像特点嵌入改进的图像输入策略和注意力模块，提出一种基于阶段注意力机制的电力线提取模型（SA-Unet）。本文提出的SA-Unet模型编码阶段采用阶段输入融合策略（Stage input fusion strategy， SIFS），充分利用图像的多尺度信息减少空间位置信息丢失。解码阶段通过嵌入阶段注意力模块（Stage attention module，SAM）聚焦电力线特征，从大量信息中快速筛选出高价值信息。实验结果表明，该方法在复杂背景的多场景中具有良好的性能。     

改进的CAGAN在虚拟试衣中的应用

针对CAGAN（Conditional Analogy GAN）换衣后效果模糊，在目标衣服与原始衣服长短不一致时效果一般，相对目标衣服保留过少的细节等问题做了相关研究并对CAGAN进行了改进，提出了新的虚拟试衣方式。经过改进的CAGAN生成一个粗糙的结果，由该结果得到目标衣服穿在模特身上改变形状后的mask，接下来利用mask对目标衣服进行变形，综合变形的衣服和第一步的结果便得到最终的试衣图像。实验结果表明，该方法解决了前面存在的问题，而且取得了非常好的效果。     

A robust model-free controller for a three-phase grid-connected photovoltaic system based on ultra-local model

In this paper, a robust model-free controller for a grid-connected photovoltaic (PV) system is designed. The system consists of a PV generator connected to a three-phase grid by a DC/AC converter. The control objectives of the overall system are to extract maximum power from the PV source, to control reactive power exchange and to improve the quality of the current injected into the grid. The model-free control technique is based on the use of an ultra-local model instead of the dynamic model of the overall system. The local model is continuously updated based on a numerical differentiator using only the input–output behavior of the controlled system. The model-free controller consists of a classical feedback controller and a compensator for the effects of internal parameter changes and external disturbances. Simulation results illustrate the efficiency of the controller for grid-connected PV systems.     

Simultaneous bioelectricity generation and pollutants removal of sediment microbial fuel cell combined with submerged macrophyte

A submerged macrophyte sediment microbial fuel cell (SP-SMFC) was constructed in this study. Ceratophyllum demersum L., Vallisneria natans, Hydrilla verticillate were chosen as the submerged plants to form cer-SMFC, val-SMFC, hyd-SMFC systems. Plant groups showed the advantage of bioelectricity generation and pollutants removal compared with the unplanted system. The cer-SMFC group stood out with the maximum power density as 24.56 mW m^?2 and the average pollutants removal in overlying water (COD: 81.16%, TN: 65.27%, TP: 79.10%) and in sediments (TN: 26.40%, TP: 21.79%). The determination of root exudates and radial oxygen loss (ROL) demonstrated that C. demersum L. was superior to other studied submerged macrophytes. More root exudates may contribute to an increase in available substrates for electrochemically active bacteria and other microorganisms. Higher enzyme activities were obtained in three SP-SMFCs (especially in cer-SMFC). ATPase and APA activities in cer-SMFC group were increased by over 40% compared with the control. The results indicated that the presence of plants enhanced the microorganism activities, thereby improving bioelectricity generation and pollutants removal. This study will facilitate the application of SP-SMFC technology as an alternative for in situ remediation of polluted sediments.     

Highly dispersed cobalt nanoparticles onto nitrogen-doped carbon nanosheets for efficient hydrogen generation via catalytic hydrolysis of sodium borohydride

Porous carbon nanostructures are promising supports for stabilizing the highly dispersed metal nanoparticles and facilitating the mass transfer during the reaction, which are critical to achieve the high efficiency of hydrogen generation from sodium borohydride dehydrogenation. Herein, the catalytically active porous architectures are simply prepared by using 2-methylimidazole and melamine as reactive sources. The structural and compositional characterizations reveal the coexistence of metallic cobalt and N-doped carbon in porous architectures. Electron microscopy observations indicate that the synthesized products are smartly constructed from the carbon nanosheets with densely dispersed Co nanoparticles. Due to the notable structural features, the prepared Co@NC-600 sample presents the highly efficient activity for catalytic hydrolysis of NaBH₄ with a hydrogen generation rate of 2574 mL min⁻¹ g_cat⁻¹ and an activation energy of 47.6 kJ mol⁻¹. The catalytically active metallic Co and suitable support-effect of N-doped carbon are responsible for catalytic dehydrogenation.     

Tungsten(VI) oxide supported rhodium nanoparticles: Highly active catalysts in hydrogen generation from ammonia borane

Herein, we report the use of tungsten(VI) oxide (WO₃) as support for Rh⁰ nanoparticles. The resulting Rh⁰/WO₃ nanoparticles are highly active and stable catalysts in H₂ generation from the hydrolysis of ammonia borane (AB). We present the results of our investigation on the particle size distribution, catalytic activity and stability of Rh⁰/WO₃ catalysts with 0.5%, 1.0%, 2.0% wt. Rh loadings in the hydrolysis reaction. The results reveal that Rh⁰/WO₃ (0.5% wt. Rh) is very promising catalyst providing a turnover frequency of 749 min^?1 in releasing 3.0 equivalent H₂ per mole of AB from the hydrolysis at 25.0 °C. The high catalytic activity of Rh⁰/WO₃ catalyst is attributed to the reducible nature of support. The report covers the results of kinetics study as well as comparative investigation of activity, recyclability, and reusability of colloidal(0) nanoparticles and Rh⁰/WO₃ (0.5 % wt. Rh) catalyst in the hydrolysis reaction.     

Hydrogen generation rate enhancement by in situ Fe(0) and nitroarene substrates in Fe3O4@Pd catalyzed ammonia borane hydrolysis and nitroarene reduction tandem reaction

We report the catalytic enhancement of hydrogen generation by 1) in situ Fe (0) formed and 2) nitroarenes substrates during Fe₃O₄@Pd core-shell nanoparticles catalyzed tandem reaction. The active hydrogen species are generated in Pd shell, which either combine to form H₂ gas or take part in relatively faster nitroarene reduction reaction. The rate of hydrogen generation from ammonia borane is dependent on the nitroarene substrate and is higher when 4-nitrophenol is used. This is due to the difference in ammonia borane adsorption on the surface of the catalyst. During recyclability, the H₂ generation rate of 2 wt% Pd loaded samples is higher than other compositions. Such an enhancement has been attributed to the formation of Fe (0) via γ-FeOOH mediated by Pd species, presumably through Pd(OH)₂. The electronic connection between Fe and Pd interface is thus shown to play an important role in the catalytic enhancement of the tandem reaction.     

Using deep learning to combine static and dynamic power analyses of cryptographic circuits

Side-channel attacks have shown to be efficient tools in breaking cryptographic hardware. Many conventional algorithms have been proposed to perform side-channel attacks exploiting the dynamic power leakage. In recent years, with the development of processing technology, static power has emerged as a new potential source for side-channel leakage. Both types of power leakage have their advantages and disadvantages. In this work, we propose to use the deep neural network technique to combine the benefits of both static and dynamic power. This approach replaces the classifier in template attacks with our proposed long short-term memory network schemes. Hence, instead of deriving a specific probability density model for one particular type of power leakage, we gain the ability of combining different leakage sources using a structural algorithm. In this paper, we propose three schemes to combine the static and dynamic power leakage. The performance of these schemes is compared using simulated test circuits designed with a 45-nm library.