Influence of oxygen vacancies on the structural,dielectric, and magnetic properties of (Mn,Co) co-doped ZnO nanostructures

We analysed the variation and effect of oxygen vacancies on the structural, dielectric and magnetic properties in case of Mn (4%) and Co (1, 2 and 4%) co-doped ZnO nanoparticles (NPs), synthesized by chemical precipitation route and annealed at 750 °C for 2 h. From the XRD, the calculated average crystallite size increased from15.30?±?0.73 nm to 16.71?±?012 nm, when Co content is increased from 1 to 4%. Enhancement of dopants (Mn, Co) introduced more and more oxygen vacancies to ZnO lattice confirmed from EDX and XPS. The high-temperature annealing leads to reduction of the dielectric properties due to enhancement in grain growth (large grain volume and lesser number of grain boundaries) with the incorporation of Co and Mn ions into the ZnO lattice. The electrical conductivity of the Mn doped and (Mn, Co) co-doped ZnO samples were enhanced due to increase in the volume of conducting grains and charge density (liberation of trapped charge carriers in oxygen vacancies and free charge carriers at higher frequencies). The Mn-doped and (Mn, Co) co-doped ZnO NPs show ferromagnetic (FM) behaviour. The saturation and remnant magnetizations (M_s and M_r) elevates from (0.235 to 1.489)?×?10^?2 and (0.12 to 0.27)?×?10^?2 emu/g while Coercivity (H_c) reduced from 97 to 36 Oe with enhancement in the concentration of dopants in ZnO matrix. Oxygen vacancies were found to be the main reason for room-temperature ferromagnetism (RTFM) in the doped and co-doped ZnO NPs. The results show that the enhanced dielectric and magnetic properties of Mn doped and (Mn, Co) co-doped ZnO is strongly correlated with the concentration of oxygen vacancies. The observed enhanced RTFM, dielectric properties and electrical conductivity makes TM doped ZnO nanoparticles suitable for spintronics, microelectronics and optoelectronics based applications.     

Investigation of charge transport mechanism in semiconducting La<Subscript>0.5</Subscript>Ca<Subscript>0.5</Subscript>Mn<Subscript>0.5</Subscript>Fe<Subscript>0.5</Subscript>O<Subscript>3</Subscript> manganite prepared by sol–gel method

Here in, we report the charge transport mechanism in semiconducting La_0.5Ca_0.5Mn_0.5Fe_0.5O₃ (LCMFO) polycrystalline material synthesized via sol–gel auto combustion route. X-ray diffraction (XRD) analysis confirmed the orthorhombic phase of the prepared material. Temperature dependent resistivity and impedance spectroscopy measurements have been carried out to probe the dielectric and electrical conduction mechanism which revealed a change of Mott variable range to the small polaronic hopping conduction mechanism around 303 K. The complex impedance and modulus spectra undoubtedly showed the contribution of both grain and grain boundary effect on the conduction properties of LCMFO. An equivalent circuit [(R_gbQ_gb) (R_gQ_g)] model has been used to address the electrical parameters associated with the different phases (grains and grain boundaries) having different relaxation times. The values of resistances of two phases obtained after fitting the equivalent circuit in the nyquist plot have been analyzed which confirmed the change of conduction mechanism around 303 K. The resultant change in conduction mechanism is also supported by the conductivity plots.     

Catalytic graphitization and formation of macroporous-activated carbon nanofibers from salt-induced and H2S-treated polyacrylonitrile

We present here a facile method to produce macroporous-activated carbon nanofibers (AMP-CNFs) by post-treating electrospun cobalt(II) chloride (CoCl₂) containing polyacrylonitrile (PAN/CoCl₂) nanofibers with hydrogen sulfide (H₂S) followed by carbonization. A range of techniques including scanning and transmission electron microscopy, FTIR and Raman spectroscopy is used to examine and characterize the process. Because of the phase behavior between carbon and cobalt, cobalt particles are formed in the nanofibers, some of which leave the fibers during the heat treatment process leading to macroporous fibrous structures. The number of the macroporous increase significantly with increasing CoCl₂ concentration in the precursor H₂S-treated PAN/CoCl₂ nanofibers. The cobalt phase in the fibers also leads to catalytic graphitization of the carbon nanofibers. The produced AMP-CNFs may be a promising candidates in many applications including anode layer in lithium ion batteries, air and liquid purifiers in filters, as well as in biomedical applications.     

A variational constitutive framework for the nonlinear viscoelastic response of a dielectric elastomer

We formulate a variational constitutive framework that accounts for nonlinear viscous behavior of electrically sensitive polymers, specifically Dielectric Elastomers (DEs), under large deformation. DEs are highly viscoelastic and their actuation response is greatly affected in dynamic applications. We used the generalized Maxwell model to represent the viscoelastic response of DE allowing the material to relax with multiple mechanisms. The constitutive updates at each load increment are obtained by minimizing an objective function formulated using the free energy and electrostatic energy of the elastomer, in addition to the viscous dissipation potential of the dashpots in each Maxwell branch. The model is then used to predict the electromechanical instability (EMI) of DE. The electro-elastic response of the DE is verified with available analytical solutions in the literature and then the material parameters are calibrated using experimental data. The model is integrated with finite element software to perform a variety of simulations on different types of electrically driven actuators under various electromechanical loadings. The electromechanical response of the DE and the critical conditions at which EMI occurs were found to be greatly affected by the viscoelasticity. Our model predicts that under a dead load EMI can be avoided if the DE operates at a high voltage rate. Subjected to constant, ramp and cyclic voltage, our model qualitatively predicts responses similar to the ones obtained from the analytical solutions and experimental data available in the literature.     

Optical,DC and AC electrical investigations of 4-hydroxy coumarin molecule as an organic Schottky diode

The current–voltage (I–V) and optical characteristics of 4-hydroxy coumarin Schottky diode were investigated. The conventional methods related with device were used to extract the various diode parameters. From dielectric study low dielectric constant and loss was observed. From its optical study, an indirect allowed transition is shown by this compound. The optical band gap (E _g ) was found to be around 3.78 eV. The observed properties shown by this molecule give a bright opportunity to explore its application for different organic devices.     

A new conservation integral for circular arc crack under multiple loads

In this paper, a path independent integral that represents the rate of energy flux at the tip during crack extension in a homogeneous and isotropic material has been derived from the principle of virtual work for a two-dimensional stationary circular arc crack subjected to multiple loads. This integral is an extension of the two-dimensional version of F-integral and includes the presence of the effects of thermal strains, initial strains and body forces, hitherto, unavailable in open literature, to the best of the authors’ knowledge. It has been further demonstrated that Rice’s J-integral, the -integral derived by Kishimoto et al. and the F-integral proposed by Lorentzon et al. are special cases of the generalized integral . The integral has been implemented into a finite element post-processing program for examining the path independence behavior under elastic and elastic-plastic deformation subjected to mechanical loads and thermo-elastic analyses under pure thermal loads. Within the limits of numerical accuracy, the application demonstrates that the solutions for the energy release rate on different contours preserve nearly identical values over the computational range.     

Dual Branches of MHD Three-Dimensional Rotating Flow of Hybrid Nanofluid on Nonlinear Shrinking Sheet

In this study, magnetohydrodynamic (MHD) three-dimensional (3D) flow of alumina (Al₂O₃) and copper (Cu) nanoparticles of an electrically conducting incompressible fluid in a rotating frame has been investigated. The shrinking surface generates the flow that also has been examined. The single-phase (i.e., Tiwari and Das) model is implemented for the hybrid nanofluid transport phenomena. Results for alumina and copper nanomaterials in the water base fluid are achieved. Boundary layer approximations are used to reduce governing partial differential (PDEs) system into the system of the ordinary differential equations (ODEs). The three-stage Lobatto IIIa method in bvp4c solver is applied for solutions of the governing model. Graphical results have been shown to examine how velocity and temperature fields are influenced by various applied parameters. It has been found that there are two branches for certain values of the suction/injection parameter b: The rise in copper volumetric concentration improved the velocity of hybrid nanofluid in the upper branch. The heat transfer rate improved for the case of hybrid nanofluid as compared to the viscous fluid and simple nanofluid.     

Traffic Queuing Management in the Internet of Things: An Optimized RED Algorithm Based Approach

Congestion control is one of the main obstacles in cyberspace traffic. Overcrowding in internet traffic may cause several problems; such as high packet hold-up, high packet dropping, and low packet output. In the course of data transmission for various applications in the Internet of things, such problems are usually generated relative to the input. To tackle such problems, this paper presents an analytical model using an optimized Random Early Detection (RED) algorithm-based approach for internet traffic management. The validity of the proposed model is checked through extensive simulation-based experiments. An analysis is observed for different functions on internet traffic. Four performance metrics are taken into consideration, namely, the possibility of packet loss, throughput, mean queue length and mean queue delay. Three sets of experiments are observed with varying simulation results. The experiments are thoroughly analyzed and the best packet dropping operation with minimum packet loss is identified using the proposed model.     

Enhance Intrusion Detection in Computer Networks Based on Deep Extreme Learning Machine

Networks provide a significant function in everyday life, and cybersecurity therefore developed a critical field of study. The Intrusion detection system (IDS) becoming an essential information protection strategy that tracks the situation of the software and hardware operating on the network. Notwithstanding advancements of growth, current intrusion detection systems also experience dif- ficulties in enhancing detection precision, growing false alarm levels and identifying suspicious activities. In order to address above mentioned issues, several researchers concentrated on designing intrusion detection systems that rely on machine learning approaches. Machine learning models will accurately identify the underlying variations among regular information and irregular information with incredible efficiency. Artificial intelligence, particularly machine learning methods can be used to develop an intelligent intrusion detection framework. There in this article in order to achieve this objective, we propose an intrusion detection system focused on a Deep extreme learning machine (DELM) which first establishes the assessment of safety features that lead to their prominence and then constructs an adaptive intrusion detection system focusing on the important features. In the moment, we researched the viability of our suggested DELMbased intrusion detection system by conducting dataset assessments and evaluating the performance factors to validate the system reliability. The experimental results illustrate that the suggested framework outclasses traditional algorithms. In fact, the suggested framework is not only of interest to scientific research but also of functional importance.     

Autonomous Parking-Lots Detection with Multi-Sensor Data Fusion Using Machine Deep Learning Techniques

The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity. Vision-based target detection and object classification have been improved due to the development of deep learning algorithms. Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise, well-engineered, and complete detection of objects, scene or events. The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection. In this study we examined to solve these problems described by (1) extracting region-of-interest in the images (2) vehicle detection based on instance segmentation, and (3) building deep learning model based on the key features obtained from input parking images. We build a deep machine learning algorithm that enables collecting real video-camera feeds from vision sensors and predicting free parking spaces. Image augmentation techniques were performed using edge detection, cropping, refined by rotating, thresholding, resizing, or color augment to predict the region of bounding boxes. A deep convolutional neural network F-MTCNN model is proposed that simultaneously capable for compiling, training, validating and testing on parking video frames through video-camera. The results of proposed model employing on publicly available PK-Lot parking dataset and the optimized model achieved a relatively higher accuracy 97.6% than previous reported methodologies. Moreover, this article presents mathematical and simulation results using state-of-the-art deep learning technologies for smart parking space detection. The results are verified using Python, TensorFlow, OpenCV computer simulation frameworks.