Tunable structural and electrical impedance properties of ordered and disordered iron oxide phases for capacitive applications

Having extensive knowledge of room temperature and temperature dependent dielectric and impedance properties of iron oxide nanostructures will help in extending the field of application from biomedical sciences to microelectronics industry. This aspect of iron oxide has long been neglected and the attention is mostly focused on magnetic investigations. To explore and extend the field of application of iron oxide this study is focused on detailed investigation of structural as well as temperature dependent (30–210?°C) dielectric along with impedance analysis. Iron oxide nanostructures are prepared using template free oleic acid assisted sol-gel method with variation in molarity of the finally synthesized sol in the range of 0.2–2.0?mM (interval 0.2?mM). Magnetite (Fe₃O₄) phase is observed at molarity of 0.2?mM whereas, vacancy ordered and disordered maghemite (γ-Fe₂O₃) phases are observed at molarities of 0.8–1.0?mM and 1.4–2.0?mM, respectively. Dielectric constant of 104.6, 74.5 and 98.43 (log f = 5.0) is observed at molarities of 0.2?mM, 1.0?mM and 2.0?mM for Fe₃O₄ and vacancy ordered (V_o) & disordered (V_d) γ-Fe₂O₃ phase, respectively. Zview software is used for the fitting of Nyquist plots. Fitted data reveals that dielectric constant strongly depends on grain boundary resistance (R_gb). Activation energy of 0.25?eV and 0.296?eV (log f = 5) is observed for Fe₃O₄ and V_d γ-Fe₂O₃ phase at 0.2?mM and 2.0?mM molarity of the final iron oxide sol.     

Development of optimized triaxially electrospun titania nanofiber-in-nanotube core-shell structure

One dimensional (1D) nanostructures and its derivatives can be manipulated to serve special functions like hollow structure, and higher surface area. 1D TiO₂ nanotube-in-nanofibers (NF@NT) are developed through triaxial electrospinning followed by a calcination process. A blended solution of polyvinyl pyrrolidone and tetra-butyl titanate is used in outer and inner layers of nanofibers, respectively, while paraffin oil is used in the middle layer. The optimized triaxial nanofibers of 669.4 ± 52.43 nm are developed at 7.5 w/w% concentration, 28 kV applied voltage, and 24 cm spinning distance. TiO₂ NF@NT structure is obtained through calcination of optimized triaxial nanofibers at 550°C. Subsequently, the morphology of TiO₂ NF@NT and its uniform diameter distribution is confirmed through scanning electron microscopy. Fourier-transform infrared spectroscopy results indicates the formation of TiO₂ NF@NT. X-Rays diffraction pattern peaks also reveals the presence of both anatase and rutile crystalline phases. The presence of only titanium (Ti) and oxygen (O) elements in the TiO₂ NF@NT is confirmed through energy dispersive X-ray spectroscopy. Brunauer–Emmett–Teller analysis indicates that TiO₂ NF@NT has a higher specific surface area of ~141.68 m²/g compared with the solid TiO₂ nanofiber (~75.31 m²/g). This study can be adopted to develop TiO₂ NF@NT for wide range of application.     

Semi-insulating polysilicon heterojunctions on silicon

The electrical characteristics of undoped semi-insulating polysilicon (SIPOS)/p-type crystalline silicon (p-Si) and N⁺-SIPOS/p-Si heterojunctions were investigated. The current-voltage characteristics of the undoped SIPOS/p-Si heterojunctions depart from a hyperbolic sine behavior as the refractive index of the SIPOS increases. Carrier conduction in the undoped heterojunctions is characterized by low- and high-temperature barrier heights, which also vary with refractive index. Current-voltage characteristics of the n⁺-SIPOS/p-Si structures were rectifying with a cut-in voltage of about 1 V, which decreased with increasing temperature. Increasing the SIPOS doping increased the current density for a given bias and reduced the cut-in voltage. The forward characteristic displayed both a low and a high field activation energy with the difference attributable to the presence of interface states at the junction. Consequently, carrier conduction in these doped and undoped SIPOS/p-Si heterojunctions appears to be controlled by the SIPOS/p-Si interface. However, hydrogen annealing passivates the interface states thereby altering the low field conduction region.     

Artificial Intelligence Based Sentence Level Sentiment Analysis of COVID-19

Web-blogging sites such as Twitter and Facebook are heavily influenced by emotions, sentiments, and data in the modern era. Twitter, a widely used microblogging site where individuals share their thoughts in the form of tweets, has become a major source for sentiment analysis. In recent years, there has been a significant increase in demand for sentiment analysis to identify and classify opinions or expressions in text or tweets. Opinions or expressions of people about a particular topic, situation, person, or product can be identified from sentences and divided into three categories: positive for good, negative for bad, and neutral for mixed or confusing opinions. The process of analyzing changes in sentiment and the combination of these categories is known as “sentiment analysis.” In this study, sentiment analysis was performed on a dataset of 90,000 tweets using both deep learning and machine learning methods. The deep learning-based model long-short-term memory (LSTM) performed better than machine learning approaches. Long short-term memory achieved 87% accuracy, and the support vector machine (SVM) classifier achieved slightly worse results than LSTM at 86%. The study also tested binary classes of positive and negative, where LSTM and SVM both achieved 90% accuracy.     

Magneto-electric characteristics in (Mn,Cu) co-doped BiFeO<Subscript>3</Subscript> multiferroic nanoparticles

Multiferroic nanoparticles having general formula BiFe_0.99-xMn_xCu_0.01O₃ (x = 0, 0.01, 0.02, 0.03 & 0.04) were prepared by a chemically derived method to explore the magneto-electric characteristics of this new class of materials. X-ray diffraction confirmed that all the samples had rhombohedraly distorted cubic perovskite 3D lattice. Lattice constant was increased with increasing concentration of Mn. Micrographs obtained from a field emission scanning electron microscope revealed a fine distribution of well-shaped particles while the particle size was increased with increased contents of Mn. Enhanced hopping mechanism induced by substitution of Mn at the lattice sites of Fe resulted in an increased AC conductivity. Ferroelectricity was observed to increase with increased Mn, attributed mainly to the leakage current due to free charge carriers instigated by multiple oxidation states of Fe and Mn. It has been observed that antiferromagnetic bismuth ferrite begins to show ferromagnetic behavior due to the collapse of antiferromagnetic spin structure with increased Mn contents.     

Machine Learning Enabled Early Detection of Breast Cancer by Structural Analysis of Mammograms

Clinical image processing plays a significant role in healthcare systems and is currently a widely used methodology. In carcinogenic diseases, time is crucial; thus, an image’s accurate analysis can help treat disease at an early stage. Ductal carcinoma in situ (DCIS) and lobular carcinoma in situ (LCIS) are common types of malignancies that affect both women and men. The number of cases of DCIS and LCIS has increased every year since 2002, while it still takes a considerable amount of time to recommend a controlling technique. Image processing is a powerful technique to analyze preprocessed images to retrieve useful information by using some remarkable processing operations. In this paper, we used a dataset from the Mammographic Image Analysis Society and MATLAB 2019b software from MathWorks to simulate and extract our results. In this proposed study, mammograms are primarily used to diagnose, more precisely, the breast’s tumor component. The detection of DCIS and LCIS on breast mammograms is done by preprocessing the images using contrast-limited adaptive histogram equalization. The resulting images’ tumor portions are then isolated by a segmentation process, such as threshold detection. Furthermore, morphological operations, such as erosion and dilation, are applied to the images, then a gray-level co-occurrence matrix texture features, Harlick texture features, and shape features are extracted from the regions of interest. For classification purposes, a support vector machine (SVM) classifier is used to categorize normal and abnormal patterns. Finally, the adaptive neuro-fuzzy inference system is deployed for the amputation of fuzziness due to overlapping features of patterns within the images, and the exact categorization of prior patterns is gained through the SVM. Early detection of DCIS and LCIS can save lives and help physicians and surgeons todiagnose and treat these diseases. Substantial results are obtained through cubic support vector machine (CSVM), respectively, showing 98.95% and 98.01% accuracies for normal and abnormal mammograms. Through ANFIS, promising results of mean square error (MSE) 0.01866, 0.18397, and 0.19640 for DCIS and LCIS differentiation during the training, testing, and checking phases.