Low threshold voltage,highly stable electroforming-free threshold switching characteristics in VOx films-based device

Threshold switching (TS) devices have evolved as one of the most promising elements in memory circuit due to their important significance in suppressing crosstalk current in the crisscross array structure. However, the issue of high threshold voltage (V_th) and low stability still restricts their potential applications. Herein, the vanadium oxide (VO_x) films deposited by the pulsed laser deposition (PLD) method are adopted as the switching layer to construct the TS devices. The TS devices with Pt/VO_x/Pt/PI structure exhibit non-polar, electroforming-free, and volatile TS characteristics with an ultralow V_th (+0.48 V/−0.48 V). Besides that, the TS devices also demonstrates high stability, without obviously performance degradations after 350 cycles of endurance measurements. Additionally, the transition mechanism is mainly attributed to the synergistic effect of metal-insulator transition of VO₂ and oxygen vacancies. Furthermore, the nonvolatile bipolar resistance switching behaviors can be obtained by changing oxygen pressure during the deposition process for switching films. This work demonstrates that vanadium oxide film is a good candidate as switching layer for applications in the TS devices and opens an avenue for future electronics.     

2.3–21 GHz broadband and high linearity distributed low noise amplifier

This paper focuses on the design of a 2.3–21 GHz Distributed Low Noise Amplifier (LNA) with low noise figure (NF), high gain (S₂₁), and high linearity (IIP3) for broadband applications. This distributed amplifier (DA) includes S/C/X/Ku/K-band, which makes it very suitable for heterodyne receivers. The proposed DA uses a 0.18 μm GaAs pHEMT process (OMMIC ED02AH) in cascade architecture with lines adaptation and equalization of phase velocity techniques, to absorb their parasitic capacitances into the gate and drain transmission lines in order to achieve wide bandwidth and to enhance gain and linearity. The proposed broadband DA achieved an excellent gain in the flatness of 13.5 ± 0.2 dB, a low noise figure of 3.44 ± 1.12 dB, and a small group delay variation of ±19.721 ps over the range of 2.3–21 GHz. The input and output reflection coefficients S₁₁ and S₂₂ are less than −10 dB. The input compression point (P_1dB) and input third-order intercept point (IIP3) are −1.5 dBm and 11.5 dBm, respectively at 13 GHz. The dissipated power is 282 mW and the core layout size is 2.2 × 0.8 mm².     

FE-SEA方法在平台支持船噪声预报中的应用

平台支持船由于作业需要通常配备有动力定位系统,其在侧推工况下舱室噪声超标较为严重。针对这个问题采用计算流体力学(CFD)方法,得到侧推螺旋桨作用在导管上的脉动压力,并将时域计算结果转换成噪声计算的激励条件。采用有限元(FE)与统计能量分析(SEA)混合方法建立船体中频段FE-SEA耦合模型并建立船体高频段SEA模型,对某65 m AHTS船侧推工况下全频段(63 Hz~8000 Hz)舱室噪声进行预报,分析该船噪声分布规律及主要影响因素。并建立起全船的SEA模型,在中频段对比SEA与FE-SEA两种方法得到的舱室声压级频谱曲线,验证了使用混合模型的必要性。     

Influence of the Impaction Angle on the Triboelectric Charging of Aerosol Nanoparticles

A low pressure impactor is used to measure triboelectric charging behavior of metallic nanoparticles. Ag nanoparticles, produced by spark discharge, were impacted onto Pt sputtered targets. The influence of the impaction angle and impaction velocity on the triboelectric charging was investigated. While for perpendicular impaction the charge transfer behavior of previous work was confirmed, the oblique impaction revealed new phenomena. Additional charge transfer was observable, which increases with obliqueness. The possibility of mass transfer between particle and target due to the high-energy collisions was also investigated. SEM characterization and Auger spectroscopy indicate mass transfer from the particle to the target surface.     

Simultaneously achieving high energy-storage density and power density under low electric fields in NBT-based ceramics

Aiming at the problem that power density and energy density are difficult to obtain simultaneously under low field, a novel composition (1-x)Na_0·5Bi_0·5TiO₃-xBaZn_1/3Ta_2/3O₃((1-x)NBT-xBZT) was designed and fabricated via solid-state methods. With the addition of BZT, the crystal lattice, structural symmetry, grain size, and dense degree were all increased proved by XRD, Raman, and Archimedes drainage method et al. Because of the enhancement of relaxor behavior, the x=0.10 sample displayed a high permittivity ε_r of 2871±15% and a low dielectric loss tan δ ≤ 0.025 in the wide temperature range of 60–400 ^oC. This ceramic also showed maximum recoverable energy density W_d (2.07 J/cm³) with high efficiency η (71.5%) under a low field of 150 kV/cm. Moreover, pulse discharge testing proved that this ceramic possessed both a significant discharge energy density W_D (0.96 J/cm³) and a record high power density P_D (108.54 MW/cm³). This work provided a promising material for high power and energy applications.     

基于GA-BP的汽车风振噪声声品质预测模型

目前对于汽车风振噪声的优化研究主要以声压级（Sound pressure level，SPL）作为单一评价指标，既不能全面反映噪声的物理属性，也无法考虑人耳对噪声的主观认知过程。为准确评价风振噪声，引入声品质，运用大涡模拟（Large eddy simulation，LES）对风振噪声进行数值仿真，根据实车道路试验判断仿真的准确性；对仿真结果进行声品质客观评价与主观评价，综合声品质客观评价参数与声品质主观评价试验结果建立BP神经网络预测模型；利用遗传算法（Genetic algorithm，GA），进一步对BP神经网络的结构参数进行优化，建立GA-BP声品质预测模型。研究结果表明，GA-BP声品质预测模型在训练速度和预测精度上都优于BP神经网络预测模型。预测模型基于声品质主客观评价结果，其预测值可以代替传统的声压级评价指标，为风振噪声提供更为准确合理的评价。     

Identifying catalyst layer compositions of proton exchange membrane fuel cells through machine-learning-based approach

Membrane electrode assembly (MEA) is considered a key component of a proton exchange membrane fuel cell (PEMFC). However, developing a new MEA to meet desired properties, such as operation under low-humidity conditions without a humidifier, is a time- and cost-consuming process. This study employs a machine-learning-based approach using K-nearest neighbor (KNN) and neural networks (NN) in the MEA development process by identifying a suitable catalyst layer (CL) recipe in MEA. Minimum redundancy maximum relevance and principal component analysis were implemented to specify the most important predictor and reduce the data dimension. The number of predictors was found to play an essential role in the accuracy of the KNN and NN models although the predictors have self-correlations. The KNN model with a K of 7 was found to minimize the model loss with a loss of 11.9%. The NN model constructed by three corresponding hidden layers with nine, eight, and nine nodes can achieve the lowest error of 0.1293 for the Pt catalyst and 0.031 for PVA as a good additive blending in the CL of the MEA. However, even if the error is low, the prediction of PVA seems to be inaccurate, regardless of the model structure. Therefore, the KNN model is more appropriate for CL recipe prediction.     

Some studies on reducing carbon dioxide emission from a CRDI engine with hydrogen and a carbon capture system

The increased use of fossil fuels in the transportation sector has led to an exponential rise of carbon dioxide in the atmosphere. The carbon dioxide (CO₂) is the major cause of global warming resulting in climate change and extreme weather conditions. This study explores the ways of reducing the CO₂ emission from the exhaust of a common rail engine. The reduction in CO₂ emissions were achieved by a combination of methods. It includes the use of low carbon biofuels (cedarwood oil (CWO), and wintergreen oil (WGO)), induction of zero-carbon, hydrogen in the intake manifold and a zeolite-based after-treatment system. In diesel, CWO and WGO were blended 20% by volume and experiments were conducted at different load conditions. The results shows that 20% blending of winter green oil resulted in maximum CO₂ reduction of 20% as compared to diesel. The emission was further reduced with the induction of hydrogen along with the after-treatment system. It is seen that a maximum of 54% reduction in CO₂ emission could be achieved with the combination for WGO in comparison to diesel without much affecting the other emissions and performance parameters.     

基于SVD和SSA-VMD降噪的轴承故障特征提取

针对强噪声背景下轴承故障特征提取困难的问题，提出一种基于奇异值分解和参数优化变分模态分解联合降噪的轴承故障特征提取方法(SSVMD)：首先，对原始信号进行奇异值分解(Singular Value Decomposition，SVD)处理，运用奇异值差分谱法选取有效奇异值并将原始信号重构得到初步降噪信号；其次，为防止故障信息丢失，将残余信号进行麻雀算法(Sparrow Search Algorithm，SSA)优化的变分模态分解(Variational Mode Decomposition，VMD）算法处理，得到最佳的模态个数K和惩罚参数α，选取峭度值最大、包络熵最小的IMF分量与初步降噪信号叠加得到最终降噪信号，并对信号进行包络分析；最后，通过仿真和试验数据分析得出，该方法能在信噪比很低的情况下降低噪声含量并提取轴承故障特征，为设备的状态监测和故障诊断提供理论依据。     

基于平均飞行航迹的CCO减噪效用研究

近年来，通过优化飞行程序降低机场飞机噪声影响成为机场环境保护的重要研究方向。文章首先建立了基于飞机“噪声-功率-距离”数据的噪声计算模型，介绍了平均飞行航迹以及连续爬升运行(Continuous Climb Opera-tion, CCO)离场程序的相关理论，最后以大型国际机场为实例，使用飞机平均飞行航迹进行噪声预测，运用综合噪声模型计算出噪声影响面积并绘制噪声影响等值线图，比较了CCO离场相对常规的标准仪表离场(Standard Instru-ment Departure, SID)的降噪效果。结果表明，CCO离场程序可有效降低机场噪声影响，在高噪声级影响区域的降噪效果更佳。