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Machine vision assessment methodology has become increasingly appealing for manufacturing automation due to innovations in noninvasive technologies such as eddy current and ultrasonic testing, which have enhanced the circumstances for bearing defect identification. At this point, manual detection results in low lifespans and reliability. So, we present an innovative rider optimization-driven mutated convolutional neural network (RO-MCNN) technique for surface defect detection of bearings based on machine vision. To evaluate the effectiveness of the suggested approach, samples of the bearing surface with various defects were gathered. The raw data specimens are denoised using a Gaussian filter, and the defect-oriented surface patterns are then extracted using a local binary pattern (LBP) technique. Subsequently, the MCNN model is designed to identify and categorize the various sorts of defects. Experimental results obtained high accuracy (99.0%), F1-score (98.7%), recall (98.6%) and precision (98.5%), which validate the greater of RO-MCNN over existing methods, demonstrating its capability in robustly detecting and classifying bearing defects with high precision and reliability, thereby advancing the efficacy of machine vision in industrial defect assessment. The MCNN model’s performance is improved and the loss function is decreased by using the RO method. The results of the experiments showed that the suggested RO-MCNN technique outperforms current strategies in terms of bearing defect type detection and classification.

A critical evaluation of high-power electronics switching in semiconductor materials is made from the standpoint of performance, reliability, and commercial viability. This study takes into account recent experimental results obtained from the field-reliability study of silicon power MOSFETs in high-density power supplies where residual material defects present in the space charge region of the device were found to generate local micro plasma that eventually caused power MOSFETs to fail. Based on these results and commercial progress made to date in wide bandgap semiconductor technologies, it is suggested that silicon carbide (SiC) promises to be the preferred material for high-power electronics switching from cost, performance and reliability considerations — this assessment is further strengthened by the near-term potential for developing large-area, low-cost, and defect-free SiC bulk substrates and epitaxial layers. This conclusion is also supported by the feasibility and the need for vertical, MOS-controlled, bipolar power switches in compact and efficient megaWatt-level power converters in order to make transformational changes in the 21st century electrical transmission and distribution infrastructure.

In this paper we report on the preparation of nanobrushes of ZnO on quartz substrate by a direct atmosphere evaporation method using Zn metal flakes. Activated charcoal was used as a catalyst that facilitated the formation of nanobrushes in which the brush stem was about 15–20 μm in length and the bristles (100–200 nm thick) were made up of nanofibrous ZnO whose tips were 10–15 nm in width and were angled in some cases. These aligned nanobrushes can find potential applications as nanopower generators and high aspect ratio AFM probes by virtue of the piezoelectric property of ZnO. This technique is simple for realizing aligned ZnO nanobrushes with metallic Zn as the source material.

This research demonstrates the capability of controlled, focused ion beam (FIB)–assisted tailoring of morphologies in both multiwall carbon nanotubes (CNTs) and Y junction nonlinear CNT systems through defect engineering. We have shown that a 30 keV FIB Ga⁺ ion beam at low ion milling currents of 1 pA can be used to partially reduce the CNT diameter, to provide electrical conduction bottleneck morphologies for linear CNTs, and to introduce both additive and subractive defects at Y junction locations of Y-CNT samples. Our aim is for this work to provide motivation for additional research to determine the effects of ion-beam-induced changes in modulating the physical and chemical properties of nanotubes.

Nanocrystalline ZnO particles substituted with different concentrations (0–30%) of Mn were synthesized by using a modified ceramic route and characterized by X-ray diffraction, transmission electron microscopy, selected area electron diffraction and energy dispersive X-ray analysis methods. Positron lifetime and coincidence Doppler broadening measurements were used as probes to identify the vacancy-type defects present in them and monitor the changes while doping. The predominant positron trapping center in the undoped ZnO is identified as the trivacancy-type cluster V_Zn+O+Zn, which is negatively charged, and it transformed to the neutral divacancy V_Zn+O on doping with Mn²⁺ ions. The intensity of the defect-specific positron lifetime component got reduced initially indicating partial occupancy of the vacancies by the doped cations but then recovered on further doping due to the additional Zn vacancies created as a result of the increasing strain introduced by the Mn ions of larger radius. The creation of a new phase ZnMn₂O₄ thereafter changed the course of variation of the annihilation parameters, as the positrons got increasingly trapped in the vacancies at the tetrahedral and octahedral sites of the spinel nanomanganite.

We report room temperature ferromagnetism (RTFM) in nanocrystalline Zn_1-xCu_xO(0.03 ≤ x ≤ 0.07) materials synthesized by autocombustion technique. The average particle sizes are in the range of 60 nm. The saturation magnetization and coercivity of 7% Cu-doped ZnO is enhanced significantly in comparison to 3% and 5% Cu-doped ZnO. There is not much variation in the optical band gap due to Cu doping, thus suggesting the uniform distribution of Cu in the ZnO matrix. Micro-Raman and photoluminescence analysis predict the presence of clusters of oxygen vacancies in Cu-doped system which improves with the increase in Cu concentration. This study provides further evidence that oxygen vacancies play an important role in the enhancement of room temperature ferromagnetic property in Cu-doped ZnO.

Spherical Bi₂S₃ nanoparticles (NPs) were prepared by a facile in situ thermal sulfuration method. Different Bi₂S₃ samples were obtained by controlling the sulfuration time. The products were characterized by X-ray diffractometer (XRD), scanning electron microscopy (SEM), Raman and Fourier-transform infrared (FT-IR) methods. The optical properties were examined by UV-visible-near-infrared (UV-Vis–NIR) and photoluminescence (PL) techniques. The results show that the phase of the products after sulfuration is pure and the spherical shape of Bi NPs has been successfully transmitted to Bi₂S₃ samples. The light absorption edges exhibit red shift to 1060 nm while the light emission displays blue shift to 868 nm, compared with the energy bandgap of bulk Bi₂S₃. The reason for the special optical properties of Bi₂S₃ NPs by this in situ sulfuration route is considered to associate with the defects and quantum size effect of NPs.

Molecular dynamics (MD) is a technique of atomistic simulation which has facilitated scientific discovery of interactions among particles since its advent in the late 1950s. Its merit lies in incorporating statistical mechanics to allow for examination of varying atomic configurations at finite temperatures. Its contributions to materials science from modeling pure metal properties to designing nanowires is also remarkable. This review paper focuses on the progress of MD in understanding the behavior of iron — in pure metal form, in alloys, and in composite nanomaterials. It also discusses the interatomic potentials and the integration algorithms used for simulating iron in the literature. Furthermore, it reveals the current progress of MD in simulating iron by exhibiting some results in the literature. Finally, the review paper briefly mentions the development of the hardware and software tools for such large-scale computations.

A novel lead zinc titanate tungsten oxide (PbZn${}_{1∕3}$Ti${}_{1∕3}$W${}_{1∕3}$O${}_3)$ single perovskite was synthesized employing a cost-effective solid-state reaction technique. A phase transition occurs from tetragonal (P4mm) to monoclinic (C2/m) after substituting zinc (Zn) and tungsten (W) into the B-site of the pure lead titanate. The average crystallite size and micro-lattice strain are 66.2nm and 0.159%, respectively, calculated by the Williamson–Hall method. The grains are uniformly distributed through well-defined grain boundaries and the average grain size is about 17.8$\mu$m analyzed from the SEM micrograph. Raman spectrum suggests the presence of all constituent elements in the sample. The UV–Visible study suggests that the sample is suitable for photovoltaic applications because of high bandgap energy $E_g=4.17$eV. The dielectric study confirms the negative temperature coefficient resistance (NTCR) behavior of the sample. The activation energy increases from 13.9meV to 142meV with a rise of temperature suggesting that ac conductivity is thermally activated. The thermally activated relaxation process was managed by immobile charge carriers at low temperatures while defects and oxygen vacancies at higher temperatures. The presence of the asymmetrical curves in modulus plots confirms the non-Debye-type behavior. Both Nyquist and Cole–Cole semi-circular arcs confirm the semiconductor nature of the sample.

The surface Partial Discharge (PD) triggered by immobilized metal wire on Gas Insulated Switchgear (GIS) insulators in terms of the discharge phenomena alongside its evaluation process was researched in this paper. With the application of gradually increased voltage, long-term tests were conducted on a well-established 252kV GIS experiment platform to observe the entire evolution process of PD from its very initiation till the eventual flashover. Ream-time measurement was undertaken during the tests to capture the trend curve as a result of test time. The results indicate that PD initiated by defects in GIS can be classified into three severity levels, namely, petty discharge, medium discharge, and threatening discharge. Moreover, a procedure based on k-means cluster analysis is proposed to diagnose and assess the severity of PD automatically.