Synthesis of dynamic g-C3N4/Fe@ZnO nanocomposites for environmental remediation applications

An effective g-C₃N₄/Fe@ZnO heterostructured photocatalyst was synthesized by a simple chemical co-precipitation method and characterized by X-ray diffraction, Fourier transform infrared spectroscopy, transmission electron microscopy and ultraviolet–visible spectroscopy. Transmission electron microscopy revealed that 7-8 nm-sized 1%Fe@ZnO nanoparticles were evenly distributed on g-C₃N₄ nanosheets to form a hybrid composite. The photocatalytic effectiveness of the composites was assessed against methylene blue dye, and it was found that the 50%g-C₃N₄/Fe@ZnO photocatalyst was more efficient in harvesting solar energy to degrade dye than the ZnO, 1%Fe@ZnO, g-C₃N₄, g-C₃N₄/ZnO and (10, 25, 40, 60 & 75 wt%) g-C₃N₄/Fe@ZnO samples. The antibacterial competency of the samples was also explored against Gram-positive (Bacillus subtilis, Staphylococcus aureus and Streptococcus salivarius) and Gram-negative (Escherichia coli) bacteria through the well diffusion method. The 50%g-C₃N₄/Fe@ZnO nanocomposite exhibited a superior antibacterial action compared to that of the rest of the samples. The exceptionally improved photocatalytic and antimicrobial efficiency of the 50%g-C₃N₄/Fe@ZnO composite was primarily accredited to the synergic outcome of the interface established between Fe@ZnO nanoparticles and g-C₃N₄ nanosheets.     

Intrinsic dielectric behavior of Mg2TiO4 spinel ceramic

In the work, we focused on the intrinsic dielectric behavior of Mg₂TiO₄ spinel ceramic by P–V–L theory and infrared spectra analysis. Ti–O bonds have larger bond ionicity values, thus playing an important role in dielectric polarization. The theoretical dielectric constant was predicted by calculating the bond susceptibility of each chemical bond. Furthermore, Ti(1)–O bonds are responsible for the structural stability of Mg₂TiO₄ ceramic. Based on classical dispersion theory, permittivity and loss corresponding to each infrared active mode were quantified, and then the crucial contribution of low-frequency modes to intrinsic dielectric properties were determined.     

同频段混合信号中的无人机信号盲检测识别

无人机信号的探测识别技术是应对无人机黑飞滥用的关键技术之一。在实际信号监测环境中，经常会接收到多个信号的混合信号，它们在时域和频域上混叠且各信号分量调制样式相同。为解决在同频段混合信号中检测识别出无人机信号的问题，提出了一种通过谱特征分析判断无人机信号存在性的方法。分别采用基于二次方谱特征的无人机图传和WiFi混合信号检测识别算法以及基于频谱带宽特征的多无人机混合信号检测识别算法，通过对射频电路采集的信号进行仿真验证，实现了从同频段混合信号中检测识别出无人机信号分量。理论分析和实验测试结果证实了所提检测识别算法的有效性。     

Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.     

Effect of Yb concentration on the microstructures,spectra, and laser performance of Yb: CaF2 transparent ceramics

Ytterbium-doped calcium fluoride transparent ceramics is considered as a promising laser gain medium. How to prepare low scattering loss ceramics is the main challenge at present. In this study, a simple route of hot-pressing method was introduced to fabricate high optical quality of Yb: CaF₂ laser ceramics with different concentration of Yb ions. The influence of Yb concentration on the particle sizes of the powders, microstructure, fluorescence spectra, output power, and laser emitting spectra of Yb: CaF₂ transparent ceramics was discussed. About 976 nm LD pumped CW laser operation has been demonstrated in 1, 2, 4, and 5 at.% Yb doped CaF₂ ceramics. A maximum average output power of 0.809 W was obtained for the 5 at.% Yb: CaF₂ ceramics with the slope efficiency was 26%.     

A novel PEM fuel cell remaining useful life prediction method based on singular spectrum analysis and deep Gaussian processes

Accurate remaining useful life (RUL) prediction of proton exchange membrane fuel cells (PEMFCs) can assess the reliability of fuel cells to determine the occurrence of failures and mitigate their operational risk. However, is it quite challenging to design a high-precision prediction method because the implicit degradation details of PEMFCs are difficult to learn well from the measurement data with high-frequency noise. Recognizing this, a novel RUL prediction method based on singular spectrum analysis (SSA) and deep Gaussian process (DGP) is proposed in this paper. The SSA-based method is firstly employed to preprocess the measurement data, which can strengthen the effective information of PEMFC degradation data at the same time remove the noise and spikes that interfere with degradation prediction. As a deep structural model, DGP has strong feature learning ability which can represent the nonlinear details of degradation data and give more accurate prediction results. At the same time, it serves as a probabilistic model that can provide the confidence interval to enhance reliability of RUL prediction. The effectiveness of the proposed method is evaluated by experimental data of the PEMFCs under steady-state conditions, and the results show that the SSA-DGP method has higher accuracy and reliability than conventional methods.     

Risk-constrained probabilistic optimal scheduling of FCPP-CHP based energy hub considering demand-side resources

Renewable energy sources (RES) with sharing a large percentage of future energy generation capacities play an essential role in the decarbonization of the future electricity and thermal networks as well as transportation sectors. However, the uncertainties in their outputs make some difficulties in making operational decisions. Hydrogen energy plays a considerable role in this concept. Besides, energy hubs (EHs) provide an efficient and reliable framework for gathering multi-type energy carriers.This paper optimally schedules the operating of the EH and decreases the emission cost, considering the electrical and thermal demand response (DR) program in a probabilistic environment. Besides plug-in electric vehicles (PEVs) and a complete model of hydrogen-based renewable energy sources are presented in the EH. Taking into account uncertainties of electrical/thermal energy markets real-time prices, customers' energy demand, and energy production of RESs into account, various scenarios are generated using the Monte Carlo simulation technique. Next, an efficient method is used to reduce the number of the scenario to make the optimization problem computable and fast. In order to reduce the risk of encountering high operating costs, the conditional value at risk (CVaR) technique is used to manage the associated risk. Simulation results show the efficiency of the proposed method in decreasing the operational cost and managing the risk of encountering unfavorable states.     

Py-GC/MS法快速鉴别PBA型聚氨酯和PCL型聚氨酯

通过热裂解气质联用(Py-GC/MS)方法对聚己二酸丁二醇酯(PBA)型聚氨酯和聚己内酯(PCL)型聚氨酯进行鉴定。考察裂解温度对PCL型聚氨酯裂解产物的影响,确定550℃为聚氨酯的最佳热裂解温度。PBA型聚氨酯热裂解谱最强的峰是环戊酮的峰,PCL型聚氨酯热裂解谱最强的峰是ε-己内酯的峰,由此可以快速鉴别PBA型聚氨酯和PCL型聚氨酯。     

高紫外反射ZnO/SiO2复合粉体制备及其热控涂层性能研究

目的 以ZnO/SiO2复合粉体为填料，开展新型热控涂层制备方法研究，提高传统白色热控涂层的紫外反射率, 研制出具有太阳全光谱高反射性能的热控涂层。方法 以硫酸锌和硅酸钠为原料，采用常温化学合成与高温热处理相结合的方法制备ZnO/SiO2复合粉体，通过X射线衍射仪、扫描电子显微镜及紫外可见分光光度计等仪器，对复合粉体的微观结构及光学性能进行表征。然后以ZnO/SiO2复合粉体为填料，以无机硅酸钾为粘结剂，制备了ZnO/SiO2热控涂料，通过不同颜基比和厚度的优化，得到了太阳全光谱(200~ 2600 nm)高反射白色热控涂层的制备方法。结果 研制的ZnO/SiO2复合粉体在紫外波段的反射率均大于88%，与传统ZnO粉体相比具有明显提升。当以无机硅酸钾为粘结剂、颜基比为2:1、喷涂厚度为100~140 μm时，所制备涂层在太阳全光谱呈现高反射特性，此时涂层的太阳吸收比为0.12，红外发射率为0.92。结论 ZnO/SiO2热控涂层具有优异的热控性能，与传统白色热控涂层相比具有更低的吸收比，同时在太阳全光谱具有高反射特性，可以满足新一代大功率卫星长寿命和高效散热的技术需求。     

高速列车转向架构架损伤、等效应力及寿命分布特性研究

为分析高速列车转向架构架损伤、等效应力及寿命分布特性，对构架疲劳关键测点进行动应力线路实测并对测点实测时域数据波形进行解析；基于实测应力时间历程及雨流计数法编制二维应力谱，利用Goodman等寿命方程将二维应力谱等效转换为一维应力谱；阐述线性累积损伤及非线性累积损伤模型的建立方法并对实测数据的线性累积损伤及非线性累积损伤进行了计算及对比分析；分别基于线性累积损伤理论及非线性累积损伤理论推导出各理论下的等效应力，基于实测数据对两种等效应力进行了计算及对比分析；通过结合非线性累积损伤及线性累积损伤理论计算的等效应力及不同可靠度下的材料S-N曲线计算并对比分析构架结构的疲劳寿命。研究结果表明，与非线性疲劳分析理论相比，线性疲劳分析理论对高速列车转向架构架的疲劳特性评估偏于保守。