Thermally Evaporated Ag–Au Bimetallic Catalysts for Efficient Electrochemical CO2 Reduction

CO₂ reduction reaction (CO₂RR) has indispensable significance for carbon recycling and renewable energy production. As typical electrochemical catalysts, Au and Ag show relatively high reaction activity and selectivity in CO₂RR. In this study, a series of Ag–Au bimetallic catalysts are designed and synthesized through the thermal evaporation method for efficient yet massive production of electrochemical catalysts. The Ag–Au catalysts show significantly enhanced activity and selectivity in CO₂RR, which is mainly attributed to the increased grain boundaries with well-dispersed single Ag atoms. After the optimization, Au20Ag10 exhibits the best performance with a CO Faraday efficiency of 89% at −0.9 V (vs the reversible hydrogen electrode) with good stability.     

HL-2M 装置真空室设计

HL-2M 真空室结构为“D”形截面的双层-薄壁-全焊接式-环状结构,内环直径 2m,外环径 5.22m, 高 3.02m,由 20 个扇形段组成而成。真空室上开设有 121 个窗口以满足真空抽气、实验诊断、辅助加热及工程安 装等方面的要求;支撑采用滑动杆式结构;材料选用 Inconel625、Inconel718 与 316L 组合。运用有限元法对真空 室进行了结构强度评估,真空室满足设计要求。     

HL-2M 真空室保温层设计及安装

基于 HL-2M 真空室烘烤保温要求,通过有限元分析和原型件实验确定采用陶瓷纤维与纳米级微孔材 料组合作为 HL-2M 真空室保温材料。在 30℃时,保温层的导热系数小于 0.027W⋅m⁻¹·℃⁻¹;300℃时,导热系数 小于 0.038W⋅m⁻¹·℃⁻¹。在保温层厚度 25mm、热面温度 300℃且达到稳态时,冷面可控制在 85℃以下,线圈侧的 温度低于 60℃,整体热损失小于 12kW,满足 HL-2M 真空室烘烤需求。      

Porcelain Insulator Crack Location and Surface States Pattern Recognition Based on Hyperspectral Technology

A porcelain insulator is an important part to ensure that the insulation requirements of power equipment can be met. Under the influence of their structure, porcelain insulators are prone to mechanical damage and cracks, which will reduce their insulation performance. After a long-term operation, crack expansion will eventually lead to breakdown and safety hazards. Therefore, it is of great significance to detect insulator cracks to ensure the safe and reliable operation of a power grid. However, most traditional methods of insulator crack detection involve offline detection or contact measurement, which is not conducive to the online monitoring of equipment. Hyperspectral imaging technology is a noncontact detection technology containing three-dimensional (3D) spatial spectral information, whereby the data provide more information and the measuring method has a higher safety than electric detection methods. Therefore, a model of positioning and state classification of porcelain insulators based on hyperspectral technology is proposed. In this model, image data were used to extract edges to locate cracks, and spectral information was used to classify the surface states of porcelain insulators with EfficientNet. Lastly, crack extraction was realized, and the recognition accuracy of cracks and normal states was 96.9%. Through an analysis of the results, it is proven that the crack detection method of a porcelain insulator based on hyperspectral technology is an effective non-contact online monitoring approach, which has broad application prospects in the era of the Internet of Things with the rapid development of electric power.     

GIS Partial Discharge Pattern Recognition Based on a Novel Convolutional Neural Networks and Long Short-Term Memory

Distinguishing the types of partial discharge (PD) caused by different insulation defects in gas-insulated switchgear (GIS) is a great challenge in the power industry, and improving the recognition accuracy of the relevant models is one of the key problems. In this paper, a convolutional neural network and long short-term memory (CNN-LSTM) model is proposed, which can effectively extract and utilize the spatiotemporal characteristics of PD input signals. First, the spatial characteristics of higher-level PD signals can be obtained through the CNN network, but because CNN is a deep feedforward neural network, it does not have the ability to process time-series data. The PD voltage signal is related to the time dimension, so LSTM saves and analyzes the previous voltage signal information, realizes the modeling of the time dependence of the data, and improves the accuracy of the PD signal pattern recognition. Finally, the pattern recognition results based on CNN-LSTM are given and compared with those based on other traditional analysis methods. The results show that the pattern recognition rate of this method is the highest, with an average of 97.9%, and its overall accuracy is better than that of other traditional analysis methods. The CNN-LSTM model provides a reliable reference for GIS PD diagnosis.     

数字束流位置探测器通道自动增益校准方法的研究与实现

在现有硬件基础上,基于BPM测量准确度的需求,在自制的电子学FPGA芯片内,通过Verilog语言实现了一种数字BPM采样数据增益自动校准的设计。首先介绍了自动增益校准模块的系统总体设计;然后对模块的实现方法做了详细说明,设计并搭建了ADC数据自动增益校准测试平台以验证自动增益较准模块的功能;最后介绍了该设计在BPM通道标定中的应用。实验结果表明,该方法可以实现4通道增益一致,使ADC采样后的数据幅度相同,有效解决了由通道增益不一致引起的测量偏差,以及工程应用中ADC数据幅度校准工作量大且难于操作的问题,将在BPM系统通道自动标定中发挥重要作用。     

细胞松弛素B阻断微核法鉴定男性个体辐射敏感性的方法初探

初步研究了男性个体辐射敏感性的鉴定方法及标准.采集50名男性志愿者的外周血,分别给予不同剂量X射线照射,采用细胞松弛素B阻断双核法测定微核率(MNF),通过二阶多项拟合法,绘制微核剂量效应选项中心标准曲线,将个人微核剂量效应曲线与标准曲线比对后判断个体辐射敏感性.0.0～2.5 Gy剂量范围内,剂量效应二阶多项拟合的中...     

质子-中子相互作用玻色子模型SU(3)算法与简单应用

质子-中子相互作用玻色子模型(简称IBM-2)具有很好的壳模型微观基础,是描述中重质量偶偶核结构的标准模型之一.对比早期建立在弱耦合U(5)基底的NPBOS算法,本文介绍基于弱耦合SU(3)基底求解IBM-2模型哈密顿量的新算法结构,通过举例典型相互作用项在SU(3)基底下矩阵元说明如何利用SU(3)群代数技术求解IB...     

Pure single line oscillation of HF lasers in fine atmospheric window

Applied Physics B - A correction to this paper has been published: https://doi.org/10.1007/s00340-021-07611-y     

一种基于谱线幅值归零的加窗插值FFT谐波检测方法

提出了一种基于谱线幅值归零的加窗插值FFT谐波检测方法用于分析复杂电网信号中的各次谐波.该检测方法将每一次在离散频谱中找到的最大谱线及其左右两侧的数根谱线幅值归零,直到各次谐波分析完毕.通过仿真实验分析了在基波频率稳定时对模拟信号的测量精度,研究了基波频率波动对谐波分析的影响.通过对EAST实验数据的分析,验证了该谐波...