Structural,Morphological, and Optical Characterization of MoO3 Thin Films and MoO3/p-Si Based Diode

Silicon - The molybdenum trioxide (MoO3) that was prepared by the solvothermal method was successfully deposited by using the conventional thermal evaporation technique. XRD and optical properties...     

Sintering and characterization of in situ formed porous cordierite ceramics with nano boehmite additions

In this study, we investigated the effect of sintering temperature and nano boehmite additions on the phase composition, densification, and mechanical properties of porous cordierite ceramics. Ceramic samples were sintered at temperatures ranging from 1200 to 1400°C. Carbon powder was used as a pore forming agent to improve the porosity of the ceramic structure. Nano boehmite and carbon additions significantly enhanced ceramic porosity and average pore size in sintered samples. The bulk density and apparent porosity of the sintered samples were found to be 0.96–1.53 g/cm³ and 42.3%–65.6%, respectively. Sintered samples had cold crushing strengths of 1.5–14.3 MPa. The microstructure obtained by scanning electron microscopy was used to measure average pore size in sintered samples and was found to be 41.93 µm for stoichiometric composition (SC), 67.72 µm for SC and nano boehmite, and 102.98 µm for SC, nano boehmite, and carbon. The microstructure of the sintered samples revealed that the crystallinity of the in situ formed phases increased with the increase in nano boehmite additions.     

Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the lungs. Chest X-ray (CXR) gained much interest after the COVID-19 outbreak thanks to its rapid imaging time, widespread availability, low cost, and portability. In radiological investigations, computer-aided diagnostic tools are implemented to reduce intra- and inter-observer variability. Using lately industrialized Artificial Intelligence (AI) algorithms and radiological techniques to diagnose and classify disease is advantageous. The current study develops an automatic identification and classification model for CXR pictures using Gaussian Filtering based Optimized Synergic Deep Learning using Remora Optimization Algorithm (GF-OSDL-ROA). This method is inclusive of preprocessing and classification based on optimization. The data is preprocessed using Gaussian filtering (GF) to remove any extraneous noise from the image’s edges. Then, the OSDL model is applied to classify the CXRs under different severity levels based on CXR data. The learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the work. OSDL model, applied in this study, was validated using the COVID-19 dataset. The experiments were conducted upon the proposed OSDL model, which achieved a classification accuracy of 99.83%, while the current Convolutional Neural Network achieved less classification accuracy, i.e., 98.14%.     

Sintering behavior of Cr2AlC MAX phase synthesized by Spark plasma sintering

In the current study, the sintering and mechanical properties of the Cr₂AlC MAX phase synthesized by Spark plasma sintering at 1000, 1100, and 1200°C were investigated. The X-ray diffraction (XRD) patterns showed that the synthesis of the Cr₂AlC MAX phase was associated with the presence of impurities such as Cr₇C₃ and Al₂O₃. On the basis of the FESEM images equipped with energy dispersive spectrometer, the MAX phases had been formed successfully and the length of these layers increased by enhancing the sintering temperature. The results of the density showed that by increasing the temperature, the density increases from 5.10 to 5.33 g/cm³ and finally decreases to 5.25 g/cm³. Vickers hardness method applied to determine the hardness of the samples showed that the hardness decreases from 8.52 to 8.07 GPa for the prepared samples.