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.     

Systematic Literature Review on Energy Efficient Routing Schemes in WSN – A Survey

Mobile Networks and Applications - In wireless sensor networks (WSN), routing is quite challenging area of research where packets are forwarded through multiple nodes to the base station. The...     

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.     

Physico-Chemical Characterization of <Emphasis Type="Italic">Moringa concanensis</Emphasis> Seeds and Seed Oil

The present work reports the characterization and comparison of Moringa concanensis seed oil from Tharparkar (a drought hit area), Pakistan. The hexane-extracted oil content of M. concanensis seeds ranged from 37.56 to 40.06% (average 38.82%). Protein, fiber, moisture and ash contents were found to be 30.07, 6.00, 5.88 and 9.00%, respectively. The extracted oil exhibited an iodine value of 67.00; a refractive index (40 °C) of 1.4648; its density (24 °C) was 0.8660 mg mL⁻¹; the saponification value (mg of KOH g⁻¹ of oil) was 179.00; unsaponifiable matter 0.78%; color (1 in. cell) 1.90R + 19.00Y; and acidity (% as oleic acid) 0.34%. Tocopherols (α, γ, and δ) in the oil accounted for 72.11, 9.26 and 33.87 mg kg⁻¹, respectively. Specific extinctions at 232 and 270 nm were 3.17 and 0.65, respectively. The peroxide and p-anisidine values of the oil were found to be 1.75 and 1.84 meq kg⁻¹, respectively. The induction periods (Rancimat, 20 L h⁻¹, 120 °C) of the crude oil was 10.81 h and reduced to 8.90 h after degumming. The M. concanensis oil was found to contain high levels of oleic acid (up to 68.00%) followed by palmitic, stearic, behenic, and arachidic acids up to levels of 11.04, 3.58, 3.44 and 7.09%, respectively. The results of the present analytical study, compared with those for other Moringa species and different vegetable oils, showed M. concanensis to be a potentially valuable non-conventional seed crop for high quality oil.     

MoSx@NiO Composite Nanostructures: An Advanced Nonprecious Catalyst for Hydrogen Evolution Reaction in Alkaline Media

The design of the earth‐abundant, nonprecious, efficient, and stable electrocatalysts for efficient hydrogen evolution reaction (HER) in alkaline media is a hot research topic in the field of renewable energies. A heterostructured system composed of MoS_x deposited on NiO nanostructures (MoS_x@NiO) as a robust catalyst for water splitting is proposed here. NiO nanosponges are applied as cocatalyst for MoS₂ in alkaline media. Both NiO and MoS₂@NiO composites are prepared by a hydrothermal method. The NiO nanostructures exhibit sponge‐like morphology and are completely covered by the sheet‐like MoS₂. The NiO and MoS₂ exhibit cubic and hexagonal phases, respectively. In the MoS_x@NiO composite, the HER experiment in 1 m KOH electrolyte results in a low overpotential (406 mV) to produce 10 mA cm^?2 current density. The Tafel slope for that case is 43 mV per decade, which is the lowest ever achieved for MoS₂‐based electrocatalyst in alkaline media. The catalyst is highly stable for at least 13 h, with no decrease in the current density. This simple, cost‐effective, and environmentally friendly methodology can pave the way for exploitation of MoS_x@NiO composite catalysts not only for water splitting, but also for other applications such as lithium ion batteries, and fuel cells.     

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.     

Exploring the Room-Temperature Ferromagnetism and Temperature-Dependent Dielectric Properties of Sr/Ni-Doped LaFeO<Subscript>3</Subscript> Nanoparticles Synthesized by Reverse Micelle Method

This paper reports the thermal, microstructural, dielectric and magnetic properties of La_0.75Sr_0.25Fe_0.65Ni_0.35O₃ nanoparticles (NPs) synthesized via reverse micelle technique. The thermogravimetric analysis of as-prepared NPs confirmed a good thermal stability of the sample. Powder x-ray diffraction data analyzed with a Rietveld refinement technique revealed single-phase and orthorhombic distorted perovskite crystal structure of the NPs having Pbnm space group. The transmission electron microscopy images show the crystalline nature and formation of nanostructures with a fairly uniform distribution of particles throughout the sample. Temperature-dependent dielectric properties of the NPs in accordance with the Kramers–Kronig transformation (KKT) model, universal dielectric response model and jump relaxation model have been discussed. Electrode or interface polarization is likely the cause of the observed dielectric behavior. Due to grain boundaries and Schottky barriers of the metallic electrodes of semiconductors, the depletion region is observed, which gives rise to Maxwell–Wagner relaxation and hence high dielectric constants. Magnetic studies revealed the ferromagnetic nature of the prepared NPs upon Sr and Ni doping in LaFeO₃ perovskite at room temperature. Therefore, these NPs could be a potential candidate as electrode material in solid oxide fuel cells.