Investigation of structural,optical, electrical,and magnetic properties of Fe-doped La0.7Sr0.3MnO3 manganites

In this work, the physical properties of nanocrystalline samples of La_0.7Sr_0.3Mn_1−xFe_xO₃ (0.0 ≤ x ≤ 0.20) perovskite manganites synthesized by the reverse micelle (RM) technique were explored in detail. The phase purity, crystal structure, and crystallite size of the samples were determined using X-ray diffraction (XRD) and Fourier transform infrared (FTIR) spectroscopy. All the samples had rhombohedral crystal structure and crystallite size increased with increase in Fe content in La_0.7Sr_0.3MnO₃. The scanning electron micrographs (SEMs) exhibited smooth surface morphology and nonuniform shape of the particles. The optical properties studied using UV-visible absorption spectroscopy revealed a decrease in the absorbance and optical band gap with an increase in Fe content in La_0.7Sr_0.3MnO₃ compound. The temperature-dependent resistivity measurements revealed semiconducting nature of x = 0 and 0.1 samples up to the studied temperature range, while a metal-to-insulator transition was observed at higher Fe doping. Magnetic studies revealed weak ferromagnetism in all the samples and a reduction in the maximum magnetization with an increase in Fe content. A close correlation between electrical transport and magnetic properties was observed with the doping of Fe ion in La_0.7Sr_0.3MnO₃ at Mn site. These results advocate strong interactions associated with the double exchange mechanism among Fe³⁺ and Mn³⁺ ions.     

Growth of ZnSe∶Mn Nanocrystals from Vapour Phase

ZnSe∶Mn nanocrystals were grown by chemical vapour transport method using the matrix of SiO2 aerogels. The cubic structure of the nanocrystals was confirmed by powder X-ray diffraction (XRD) and electron diffraction (ED) studies. The size of the crystals was observed using transmission electron microscope (TEM). The oxidation state of Mn in ZnSe nanocrystal was found using electron spin resonance (ESR) spectrum. The room temperature luminescence measurements show the peaks corresponding to both bandgap of the material and Mn in ZnSe.     

A hybrid technique of modeling of cracks using displacement discontinuity and direct boundary element method

A hybrid technique to model two dimensional fracture problems which makes use of displacement discontinuity and direct boundary element method is presented. Direct boundary element method is used to model the finite domain of the body, while displacement discontinuity elements are utilized to represent the cracks. Thus the advantages of the component methods are effectively combined. This method has been implemented in a computer program and numerical results which show the accuracy of the present method are presented. The cases of bodies containing edge cracks as well as multiple cracks are considered. A direct method and an iterative technique are described. The present hybrid method is most suitable for modeling problems involving crack propagation.     

A novel Jacob spectral method for multi-dimensional weakly singular nonlinear Volterra integral equations with nonsmooth solutions

The main purpose of this work is to develop a spectrally accurate collocation method for solving weakly singular integral equations of the second kind with nonsmooth solutions in high dimensions. The proposed spectral collocation method is based on a multivariate Jacobi approximation in the frequency space. The essential idea is to adopt a smoothing transformation for the spectral collocation method to circumvent the curse of singularity at the beginning of time. As such, the singularity of the numerical approximation can be tailored to that of the singular solutions. A rigorous convergence analysis is provided and confirmed by numerical tests with nonsmooth solutions in two dimensions. The results in this paper seem to be the first spectral approach with a theoretical justification for high-dimensional nonlinear weakly singular Volterra type equations with nonsmooth solutions.

     

Conceptual design of a novel hybrid fuel cell/desalination system

A novel concept for integrating fuel cells with desalination systems is proposed and investigated in this work. Two unique case studies are discussed — the first involving a hybrid system with a reverse osmosis (RO) unit and the second — integrating with a thermal desalination process such as multi-stage flash (MSF). The underlying motivation for this system integration is that the exhaust gas from a hybrid power plant (fuel cell/turbine system) contains considerable amount of thermal energy, which may be utilized for desalination units. This exhaust heat can be suitably used for preheating the feed in desalination processes such as reverse osmosis which not only increases the potable water production, but also decreases the relative energy consumption by approximately 8% when there is an increase of just 8°C rise in temperature. Additionally, an attractive hybrid system application which combines power generation at 70%+ system efficiency with efficient waste heat utilization is thermal desalination. In this work, it is shown that the system efficiency can be raised appreciably when a high-temperature fuel cell co-generates DC power in-situ with waste heat suitable for MSF. Results indicate that such hybrid system could show a 5.6% increase in global efficiency. Such combined hybrid systems have overall system efficiencies (second-law base) exceeding those of either fuel-cell power plants or traditional desalination plants.     

Cognitive-Mathematical Approaches for Evaluating Architectural Contextual Fit

The main goal of this study is to apply a scientific quantitative approach to the investigation of contextual fit. This is approached mathematically within the framework of cognitive science and research on categorization and prototypes. Two experiments investigated two leading mathematical-cognitive approaches for explaining people’s judgment of contextual fit of a new building with an architectural/urban context: prototype approach and feature frequency approach. The basic concept is that people represent the built environment via architectural prototypes and/or frequencies of encountered architectural features. In the first experiment, a group of twelve participants performed rank order tasks on artificially created architectural patterns, for the purpose of psychological scaling. Perceptual distances among all patterns were mathematically determined. In the second experiment, three groups of architectural patterns were constructed to represent assumed architectural contexts. The prototype of each context was mathematically determined according to prototype cognitive model, and based on the distances calculated in the first experiment. Fifty-six students participated in the main experiment, in which they rank ordered a group of fifteen architectural patterns in terms of contextual fit to each of the three architectural contexts. Participants’ rank order data of the fifteen patterns were regressed on both the perceptual distances from prototypes, and numbers of features shared with each architectural context. Results indicated that both prototype and feature frequency approaches significantly accounted for important portions of participants’ judgments. However, participants tended to prefer one approach to the other according to context composition. Results have implications for both research on utilizing cognitive-mathematical models in architectural research and on urban design guidelines and control.     

Design,simulation and analysis of RF MEMS capacitive shunt switches with high isolation and low pull-in-voltage

Microsystem Technologies - This paper presents the design a capacitive shunt type RF-MEMS switch with high isolation, high switching speed and low actuation voltage for Ka-band applications. The...     

Nutratherapeutics approach against cancer: tomato‐mediated synthesised gold nanoparticles

In this study, an eco‐friendly biosynthesis of stable gold nanoparticles (T‐GNPs) was carried out using different concentrations of tomato juice (nutraceuticals) as a reducing agent and tetrachloroauric acid as a metal precursor to explore their potential application in cancer therapeutics. The synthesis of T‐GNPs was monitored by UV‐visible absorption spectroscopy, which unveiled their formation by exhibiting the typical surface plasmon absorption maxima at 522 nm. The size of T‐GNPs was found to be 10.86 ± 0.6 nm. T‐GNPs were characterised by dynamic light scattering, zeta potential, transmission electron microscopy analysis and Fourier transform infrared spectroscopy. T‐GNPs were further investigated for their anti‐cancer activity against human lung carcinoma cell line (A ₅₄₉) and human cervical cancer cell line wherein the IC₅₀ values were found to be 0.286 and 0.200 mM, respectively. T‐GNPs inhibited the growth of cancer cells by generating ROS and inducing apoptosis. T‐GNPs were found highly effective by virtue of their size, metallic property and capping molecules. Thus, this study opens up the prospects of using nutraceutical (tomato juice) as nutratherapeutic agent (T‐GNPs) against critical diseases like lung cancer and cervical cancer.Inspec keywords: gold, nanoparticles, particle size, cancer, ultraviolet spectra, visible spectra, electrokinetic effects, transmission electron microscopy, Fourier transform infrared spectra, cellular biophysics, spectrochemical analysis, nanomedicine, nanofabricationOther keywords: tomato‐mediated synthesised gold nanoparticles, tomato juice, reducing agent, tetrachloroauric acid, cancer therapeutics, UV‐visible absorption spectroscopy, surface plasmon absorption, dynamic light scattering, zeta potential, transmission electron microscopy analysis, Fourier transform infrared spectroscopy, human lung carcinoma cell line, anticancer activity, human cervical cancer cell line, nutratherapeutic agent, lung cancer, Au     

Optimized Identification with Severity Factors of Gastric Cancer for Internet of Medical Things

The Internet of Medical Things (IoMT) emerges with the vision of the Wireless Body Sensor Network (WBSN) to improve the health monitoring systems and has an enormous impact on the healthcare system for recognizing the levels of risk/severity factors (premature diagnosis, treatment, and supervision of chronic disease i.e., cancer) via wearable/electronic health sensor i.e., wireless endoscopic capsule. However, AI-assisted endoscopy plays a very significant role in the detection of gastric cancer. Convolutional Neural Network (CNN) has been widely used to diagnose gastric cancer based on various feature extraction models, consequently, limiting the identification and categorization performance in terms of cancerous stages and grades associated with each type of gastric cancer. This paper proposed an optimized AI-based approach to diagnose and assess the risk factor of gastric cancer based on its type, stage, and grade in the endoscopic images for smart healthcare applications. The proposed method is categorized into five phases such as image pre-processing, Four-Dimensional (4D) image conversion, image segmentation, K-Nearest Neighbour (K-NN) classification, and multi-grading and staging of image intensities. Moreover, the performance of the proposed method has experimented on two different datasets consisting of color and black and white endoscopic images. The simulation results verified that the proposed approach is capable of perceiving gastric cancer with 88.09% sensitivity, 95.77% specificity, and 96.55% overall accuracy respectively.     

Development and Validation of a Simple Reversed‐Phase HPLC‐UV Method for Determination of Malondialdehyde in Olive Oil

A simple, precise, accurate and selective method was developed and validated for determination of malondialdehyde (MDA) in olive oil. Separation was achieved on a reversed‐phase C₈ column using a mobile phase consisting of methanol/0.8 % phosphoric acid (10:90, v/v), at a flow rate of 1.0 ml/min and UV detection at 220 nm. This method was validated according to the requirements for new methods, which include accuracy, precision, selectivity, robustness, a limit of detection, limit of quantitation (LOQ), linearity and range. The current method demonstrated good linearity over the range of 0.5–1000 ppm of MDA with r² greater than 0.999. The recovery of MDA in olive oil ranged from 97.1 to 99.1 %. The method was selective where MDA was distinctly separated from other compounds of the oil with good resolution. The method was also precise where the RSD of the peak areas of replicate injections of MDA standard solution were less than 1 %. The degree of reproducibility of the results obtained as a result of small deliberate variations in the method parameters and by changing the analytical operators proved that the method is robust and rugged. The low LOQ of MDA (0.5 ppm) using this method enables quantitation of MDA at low concentration.