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.     

The relation between the radii and the densities of magnetic skyrmions

Compared with the traditional magnetic bubble, a skyrmion has a smaller size, and better stability and therefore is considered as a very promising candidate for future memory devices. When skyrmions are manipulated, erased and created, the density of skyrmions can be varied, however the relationship between the radii and the densities of skyrmions needs more exploration. In this paper, we study this problem both theoretically and by using the lattice simulation. The average radius of skyrmions as a function of material parameters, the strength of the external magnetic field and the density of skyrmions is obtained and verified. With this explicit function, the skyrmion radius can be easily predicted, which is helpful for the future study of skyrmion memory devices.     

P213 BN: a novel large-cell boron nitride polymorph

A new boron nitride polymorph, P2₁3 BN (space group: P2₁3), is investigated by first-principles calculations, including its structural properties, stability, elastic properties, anisotropy and electronic properties. It is found that the new boron nitride polymorph P2₁3 BN is mechanically, dynamically and thermodynamically stable. The bulk modulus (B), shear modulus (G) and Young's modulus of P2₁3 BN are 91 GPa, 41 GPa and 107 GPa, respectively, all of which are larger than that of Y carbon and TY carbon. By comparing with c-BN, the Young's modulus, shear modulus and Poisson's ratio of P2₁3 BN show tiny anisotropy in the (001), (010), (100) and (111) planes. At the same time, in contrast with most boron nitride polymorphs, P2₁3 BN is a semiconductor material with a smaller band gap of 1.826 eV. The Debye temperature and the anisotropic sound velocities of P2₁3 BN are also investigated in this work.     

Experimental Study of Effect of Temperature Variations on the Impedance Signature of PZT Sensors for Fatigue Crack Detection

Structural health monitoring (SHM) is recognized as an efficient tool to interpret the reliability of a wide variety of infrastructures. To identify the structural abnormality by utilizing the electromechanical coupling property of piezoelectric transducers, the electromechanical impedance (EMI) approach is preferred. However, in real-time SHM applications, the monitored structure is exposed to several varying environmental and operating conditions (EOCs). The previous study has recognized the temperature variations as one of the serious EOCs that affect the optimal performance of the damage inspection process. In this framework, an experimental setup is developed in current research to identify the presence of fatigue crack in stainless steel (304) beam using EMI approach and estimate the effect of temperature variations on the electrical impedance of the piezoelectric sensors. A regular series of experiments are executed in a controlled temperature environment (25°C–160°C) using 202 V1 Constant Temperature Drying Oven Chamber (Q/TBXR20-2005). It has been observed that the dielectric constant ε₃₃^T which is recognized as the temperature-dependent constant of PZT sensor has sufficiently influenced the electrical impedance signature. Moreover, the effective frequency shift (EFS) approach is optimized in term of significant temperature compensation for the current impedance signature of PZT sensor relative to the reference signature at the extended frequency bandwidth of the developed measurement system with better outcomes as compared to the previous literature work. Hence, the current study also deals efficiently with the critical issue of the width of the frequency band for temperature compensation based on the frequency shift in SHM. The results of the experimental study demonstrate that the proposed methodology is qualified for the damage inspection in real-time monitoring applications under the temperature variations. It is capable to exclude one of the major reasons of false fault diagnosis by compensating the consequence of elevated temperature at extended frequency bandwidth in SHM.     

深海直达波区卷积神经网络测距方法

深海声场通常可以看作不同掠射角的多途声线在接收器处的叠加,其中经海底反射的声线携带与海底参数有关的声场特征。利用深度卷积神经网络分别学习垂直阵声压域(CNN-Field)和垂直阵波束域(CNN-CBF)特征的方法被用来估计直达波区声源距离。该方法首先对仿真直达波区声场数据做预处理,然后将声压域和波束域的声场数据分别作为训练集训练深度卷积神经网络模型,最后输入测试集数据到训练完成的模型中估计声源距离.实测环境参数的仿真实验表明CNN-Field方法在不同海底参数的测试集下测距结果差异较大,CNN-CBF方法差异较小,而且在16阵元10 m等间距垂直阵的阵元域信噪比大于0dB时估计准确率可以达到97%.海试数据处理结果表明CNN-CBF方法的直达波区内测距准确率高于CNN-Field,在距离10 km以内的平均准确率可以达到93.16%.     

BiVO4/TiO2 heterojunction with rich oxygen vacancies for enhanced electrocatalytic nitrogen reduction reaction

The large-scale production of ammonia mainly depends on the Haber–Bosch process, which will lead to the problems of high energy consumption and carbon dioxide emission. Electrochemical nitrogen fixation is considered to be an environmental friendly and sustainable process, but its efficiency largely depends on the activity and stability of the catalyst. Therefore, it is imperative to develop highefficient electrocatalysts in the field of nitrogen reduction reaction (NRR). In this paper, we developed a BiVO₄/TiO₂ nanotube (BiVO₄/TNT) heterojunction composite with rich oxygen vacancies as an electrocatalytic NRR catalyst. The heterojunction interface and oxygen vacancy of BiVO₄/TNT can be the active site of N₂ dynamic activation and proton transition. The synergistic effect of TiO₂ and BiVO₄ shortens the proton transport path and reduces the over potential of chemical reaction. BiVO₄/TNT has high ammonia yield of 8.54 μg·h⁻¹·cm⁻² and high Faraday efficiency of 7.70% in −0.8 V vs. RHE in 0.1 M Na₂SO₄ solution.     

Refined Composite Multi-Scale Reverse Weighted Permutation Entropy and Its Applications in Ship-Radiated Noise

Ship-radiated noise is one of the important signal types under the complex ocean background, which can well reflect physical properties of ships. As one of the valid measures to characterize the complexity of ship-radiated noise, permutation entropy (PE) has the advantages of high efficiency and simple calculation. However, PE has the problems of missing amplitude information and single scale. To address the two drawbacks, refined composite multi-scale reverse weighted PE (RCMRWPE), as a novel measurement technology of describing the signal complexity, is put forward based on refined composite multi-scale processing (RCMP) and reverse weighted PE (RWPE). RCMP is an improved method of coarse-graining, which not only solves the problem of single scale, but also improves the stability of traditional coarse-graining; RWPE has been proposed more recently, and has better inter-class separability and robustness performance to noise than PE, weighted PE (WPE), and reverse PE (RPE). Additionally, a feature extraction scheme of ship-radiated noise is proposed based on RCMRWPE, furthermore, RCMRWPE is combined with discriminant analysis classifier (DAC) to form a new classification method. After that, a large number of comparative experiments of feature extraction schemes and classification methods with two artificial random signals and six ship-radiated noise are carried out, which show that the proposed feature extraction scheme has better performance in distinguishing ability and stability than the other three similar feature extraction schemes based on multi-scale PE (MPE), multi-scale WPE (MWPE), and multi-scale RPE (MRPE), and the proposed classification method also has the highest recognition rate.     

Intelligent Fault Identification for Rolling Bearings Fusing Average Refined Composite Multiscale Dispersion Entropy-Assisted Feature Extraction and SVM with Multi-Strategy Enhanced Swarm Optimization

Rolling bearings act as key parts in many items of mechanical equipment and any abnormality will affect the normal operation of the entire apparatus. To diagnose the faults of rolling bearings effectively, a novel fault identification method is proposed by merging variational mode decomposition (VMD), average refined composite multiscale dispersion entropy (ARCMDE) and support vector machine (SVM) optimized by multistrategy enhanced swarm optimization in this paper. Firstly, the vibration signals are decomposed into different series of intrinsic mode functions (IMFs) based on VMD with the center frequency observation method. Subsequently, the proposed ARCMDE, fusing the superiorities of DE and average refined composite multiscale procedure, is employed to enhance the ability of the multiscale fault-feature extraction from the IMFs. Afterwards, grey wolf optimization (GWO), enhanced by multistrategy including levy flight, cosine factor and polynomial mutation strategies (LCPGWO), is proposed to optimize the penalty factor C and kernel parameter g of SVM. Then, the optimized SVM model is trained to identify the fault type of samples based on features extracted by ARCMDE. Finally, the application experiment and contrastive analysis verify the effectiveness of the proposed VMD-ARCMDE-LCPGWO-SVM method.     

Research on a Rice Counting Algorithm Based on an Improved MCNN and a Density Map

The thousand grain weight is an index of size, fullness and quality in crop seed detection and is an important basis for field yield prediction. To detect the thousand grain weight of rice requires the accurate counting of rice. We collected a total of 5670 images of three different types of rice seeds with different qualities to construct a model. Considering the different shapes of different types of rice, this study used an adaptive Gaussian kernel to convolve with the rice coordinate function to obtain a more accurate density map, which was used as an important basis for determining the results of subsequent experiments. A Multi-Column Convolutional Neural Network was used to extract the features of different sizes of rice, and the features were fused by the fusion network to learn the mapping relationship from the original map features to the density map features. An advanced prior step was added to the original algorithm to estimate the density level of the image, which weakened the effect of the rice adhesion condition on the counting results. Extensive comparison experiments show that the proposed method is more accurate than the original MCNN algorithm.