Some peculiarities of electric discharge between a solid electrode and technical water

We present the results of experimental study of the electric discharge between metal electrodes of various geometry and technical water within the pressure range of 8 × 10³–10⁵ Pa at the saw-tooth voltage generator frequency, f = 40 MHz, and the interelectrode distance, l = 3–30 mm. We consider transfer of the streamer discharge into spark one depending on the geometry of the metal electrode and its material. We investigate the electrical characteristics of the discharge between the plate electrode and the technical water within a wide pressure range. The essential influence of the streamer discharge type on the ozone release within the investigated parameters range is discovered.     

Effect of Initial Annealing Temperature on Microstructural Development and Microhardness in High‐Purity Copper Processed by High‐Pressure Torsion

The effect of the initial annealing temperature on the evolution of microstructure and microhardness in high purity OFHC Cu is investigated after processing by HPT. Disks of Cu are annealed for 1 h at two different annealing temperatures, 400 and 800 °C, and then processed by HPT at room temperature under a pressure of 6.0 GPa for 1/4, 1/2, 1, 5, and 10 turns. Samples are stored for 6 months after HPT processing to examine the self‐annealing effects. Electron backscattered diffraction (EBSD) measurements are recorded for each disk at three positions: center, mid‐radius, and near edge. Microhardness measurements are also recorded along the diameters of each disk. Both alloys show rapid hardening and then strain softening in the very early stages of straining due to self‐annealing with a clear delay in the onset of softening in the alloy initially annealed at 800 °C. This delay is due to the relatively larger initial grain size compared to the alloy initially annealed at 400 °C. The final microstructures consist of homogeneous fine grains having average sizes of ≈0.28 and ≈0.34 µm for the alloys initially annealed at 400 and 800 °C, respectively. A new model is proposed to describe the behavior of the hardness evolution by HPT in high purity OFHC Cu.     

High Efficiency Poly(acrylonitrile) Electrospun Nanofiber Membranes for Airborne Nanomaterials Filtration

The potential of poly(acrylonitrile) electrospun membranes with tuneable pore size and fiber distributions were investigated for airborne fine‐particle filtration for the first time. The impact of solution concentration on final membrane properties are evaluated for the purpose of designing separation materials with higher separation efficiency. The properties of fibers and membranes are investigated systematically: the average pore distribution, as characterized by capillary flow porometry, and thermo‐mechanical properties of the mats are found to be dependent on fiber diameter and on specific electrospinning conditions. Filtration efficiency and pressure drop are calculated from measurement of penetration through the membranes using potassium chloride (KCl) aerosol particles ranging from 300 nm to 12 μm diameter. The PAN membranes exhibited separation efficiencies in the range of 73.8–99.78% and a typical quality factor 0.0224 (1 Pa^?1) for 12 wt% PAN with nanofibers having a diameter of 858 nm. Concerning air flow rate, the quality factor and filtration efficiency of the electrospun membranes at higher face velocity are much more stable than for commercial membranes. The results suggest that the structure of electrospun membranes is the best for air filtration in terms of filtration stability at high air flow rate.

     

Single Nanoparticle Magnetic Spin Memristor

There is an increasing demand for the development of a simple Si‐based universal memory device at the nanoscale that operates at high frequencies. Spin‐electronics (spintronics) can, in principle, increase the efficiency of devices and allow them to operate at high frequencies. A primary challenge for reducing the dimensions of spintronic devices is the requirement for high spin currents. To overcome this problem, a new approach is presented that uses helical chiral molecules exhibiting spin‐selective electron transport, which is called the chiral‐induced spin selectivity (CISS) effect. Using the CISS effect, the active memory device is miniaturized for the first time from the micrometer scale to 30 nm in size, and this device presents memristor‐like nonlinear logic operation at low voltages under ambient conditions and room temperature. A single nanoparticle, along with Au contacts and chiral molecules, is sufficient to function as a memory device. A single ferromagnetic nanoplatelet is used as a fixed hard magnet combined with Au contacts in which the gold contacts act as soft magnets due to the adsorbed chiral molecules.     

Optimal Deep Learning Model Enabled Secure UAV Classification forIndustry 4.0

Emerging technologies such as edge computing, Internet of Things (IoT), 5G networks, big data, Artificial Intelligence (AI), and Unmanned Aerial Vehicles (UAVs) empower, Industry 4.0, with a progressive production methodology that shows attention to the interaction between machine and human beings. In the literature, various authors have focused on resolving security problems in UAV communication to provide safety for vital applications. The current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification (CSODL-SUAVC) model for Industry 4.0 environment. The suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such as secure communication via image steganography and image classification. Primarily, the proposed CSODL-SUAVC method involves the following methods such as Multi-Level Discrete Wavelet Transformation (ML-DWT), CSO-related Optimal Pixel Selection (CSO-OPS), and signcryption-based encryption. The proposed model deploys the CSO-OPS technique to select the optimal pixel points in cover images. The secret images, encrypted by signcryption technique, are embedded into cover images. Besides, the image classification process includes three components namely, Super-Resolution using Convolution Neural Network (SRCNN), Adam optimizer, and softmax classifier. The integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance upon UAV communication. The proposed CSODL-SUAVC model was experimentally validated using benchmark datasets and the outcomes were evaluated under distinct aspects. The simulation outcomes established the supreme better performance of the CSODL-SUAVC model over recent approaches.     

Infrared Spectroscopy-Based Chemometric Analysis for Lard Differentiation in Meat Samples

One of the most pressing concerns for the consumer market is the detection of adulteration in meat products due to their preciousness. The rapid and accurate identification mechanism for lard adulteration in meat products is highly necessary, for developing a mechanism trusted by consumers and that can be used to make a definitive diagnosis. Fourier Transform Infrared Spectroscopy (FTIR) is used in this work to identify lard adulteration in cow, lamb, and chicken samples. A simplified extraction method was implied to obtain the lipids from pure and adulterated meat. Adulterated samples were obtained by mixing lard with chicken, lamb, and beef with different concentrations (10%–50% v/v). Principal component analysis (PCA) and partial least square (PLS) were used to develop a calibration model at 800–3500 cm⁻¹. Three-dimension PCA was successfully used by dividing the spectrum in three regions to classify lard meat adulteration in chicken, lamb, and beef samples. The corresponding FTIR peaks for the lard have been observed at 1159.6, 1743.4, 2853.1, and 2922.5 cm⁻¹, which differentiate chicken, lamb, and beef samples. The wavenumbers offer the highest determination coefficient R² value of 0.846 and lowest root mean square error of calibration (RMSEC) and root mean square error prediction (RMSEP) with an accuracy of 84.6%. Even the tiniest fat adulteration up to 10% can be reliably discovered using this methodology.     

Second Sound Measurements Near the Tricritical Point in 3He-4He Mixtures

In this paper we discuss a pulsed second sound experiment, aimed at determining accurately the critical exponent , and the predicted logarithmic correction to scaling, for the superfluid density along a tricritical path in the ³ He- ⁴ He phase diagram. We present an accurate estimate for the limits for closest approach to the tricritical point, as set by gravitationally induced sample inhomogeneities and finite size effects, and discuss some of the complications associated with measurements close to the tricritical point.