Central Electronics Engineering Research Institute

About: Central Electronics Engineering Research Institute is a facility organization based out in Pilāni, India. It is known for research contribution in the topics: Gyrotron & Thin film. The organization has 951 authors who have published 1280 publications receiving 8940 citations.

Oxide Materials for Development of Integrated Gas Sensors—A Comprehensive Review

Abstract: In the recent past a great deal of research efforts were directed toward the development of miniaturized gas-sensing devices, particularly for toxic gas detection and for pollution monitoring. Though various techniques are available for gas detection, solid state metal oxides offer a wide spectrum of materials and their sensitivities for different gaseous species, making it a better choice over other options. In this article a critical parameter analysis of different metal oxides that are known to be sensitive to various gaseous species are thoroughly examined. This includes phase of the oxide, sensing gaseous species, operating temperature range, and physical form of the material for the development of integrated gas sensors. The oxides that are covered in this study include oxides of aluminum, bismuth, cadmium, cerium, chromium, cobalt, copper, gallium, indium, iron, manganese, molybdenum, nickel, niobium, ruthenium, tantalum, tin, titanium, tungsten, vanadium, zinc, zirconium, and the mixed or...

Thin-layer modelling of black tea drying process

Abstract: An experimental dryer was developed for determining the kinetics of black tea drying. Drying characteristics of tea were examined using heated ambient air for the temperature range 80–120°C and air flow velocity range 0.25–0.65 m/s. The data of sample weight, dry- and wet-bulb temperatures and air velocity of the drying air were recorded continuously during each test. The drying data were then fitted to the different semi-theoretical models such as Lewis, Page, modified Page, two-term and Henderson and Pabis models, based on the ratios of the difference between the initial and final moisture contents and the equilibrium moisture content. The Lewis model gave better predictions than other models, and satisfactorily described the thin-layer drying characteristics of black tea particles. The effective diffusivity varied from 1.14×10 −11 to 2.98×10 −11 m 2 /s over the temperature range. The temperature dependence of the diffusivity coefficient was described by the Arrhenius-type relationship. The activation energy for moisture diffusion was found to be 406.02 kJ/mol. Temperature and air velocity dependence on drying constant was described by the Arrhenius-type and Power-type relationships. The coefficients of determination were above 0.996 for both relationships. The Arrhenius-type model was used to predict the acceptable moisture ratios at the experimental drying conditions and to understand better the influence of drying variables on drying rate constant. The results illustrate that in spite of high initial moisture content, the drying of tea particles takes place only in the falling rate period. This single-layer drying equation can be used for the simulation of deep-bed drying of black tea.

FNDNet – A deep convolutional neural network for fake news detection

Abstract: With the increasing popularity of social media and web-based forums, the distribution of fake news has become a major threat to various sectors and agencies. This has abated trust in the media, leaving readers in a state of perplexity. There exists an enormous assemblage of research on the theme of Artificial Intelligence (AI) strategies for fake news detection. In the past, much of the focus has been given on classifying online reviews and freely accessible online social networking-based posts. In this work, we propose a deep convolutional neural network (FNDNet) for fake news detection. Instead of relying on hand-crafted features, our model (FNDNet) is designed to automatically learn the discriminatory features for fake news classification through multiple hidden layers built in the deep neural network. We create a deep Convolutional Neural Network (CNN) to extract several features at each layer. We compare the performance of the proposed approach with several baseline models. Benchmarked datasets were used to train and test the model, and the proposed model achieved state-of-the-art results with an accuracy of 98.36% on the test data. Various performance evaluation parameters such as Wilcoxon, false positive, true negative, precision, recall, F1, and accuracy, etc. were used to validate the results. These results demonstrate significant improvements in the area of fake news detection as compared to existing state-of-the-art results and affirm the potential of our approach for classifying fake news on social media. This research will assist researchers in broadening the understanding of the applicability of CNN-based deep models for fake news detection.

Electrochemical sensing and remediation of 4-nitrophenol using bio-synthesized copper oxide nanoparticles

Abstract: The present work reports impedance based electrochemical sensing and remediation of 4-nitro phenol (4-NP) using biosynthesized (CuO) copper oxide nanoparticles. The synthesis of CuO nanoparticles is achieved using fruit extract of plant Fortunella japonica as reducing and stabilizing agent. The CuO nanoparticles were characterized using various analytical techniques like UV–Visible spectroscopy, Atomic force microscopy (AFM), High resolution transmission electron microscopy (HR-TEM), Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, and X-ray diffraction (XRD). For electrochemical sensing of 4-NP, the CuO nanoparticles were drop casted on screen printed electrode (SPE) and electrode is referred at SPE/CuONPs sensor. The mechanism of 4-NP redox reactions was examined using cyclic voltammetry (CV). The electrochemical sensing of 4-NP has been done using square wave voltammetry (SWV) and impedance spectroscopy. In SWV, the oxidation peak current increased with increase in the concentration of 4-NP from 10 nM to 10 mM having regression coefficient of 0.996. In impedometric sensing, change in charge transfer resistance (Rct) with change in 4-NP concentration was used as a signal. The Rct decreased with increase in 4-NP concentration which is in accordance with SWV results. The effect of solution pH on impedometric response of SPE/CuONPs sensor was also evaluated. The SPE/CuONPs sensor exhibited good reproducibility and selectivity towards the analyte and is able to perform real sample analysis. The CuO nanoparticles act as a catalyst and showed good degradation percentage of 4-NP pollutant.

A Review on the Applications of High Power, High Frequency Microwave Source: Gyrotron

Abstract: The gyro oscillators or simply gyrotrons are used in a variety of applications where high electromagnetic power is required at millimeter/submillimeter wave frequencies. The research on the gyrotron microwave tube was initiated by the demand of high power, high frequency electromagnetic wave source in the magnetically confined plasma fusion application. Since the initial phase of gyrotron development, new thrust areas have been explored by the several research groups. The gyrotron shows several unique advantages as a high power source compare to the other millimeter/submillimeter wave sources either semiconductor based devices or vacuum based tube devices. At present gyrotron is used successfully in the field of plasma heating, plasma diagnosis, medical spectroscopy, material processing, whether monitoring etc. discussed in detail in this article. Several new fields of technology like security, metal joining, planetary defense etc. are under exploration for the futuristic use of gyrotron. In this review article, the applications of gyrotron, various issues remained in the further modification of the device, global scenario of the gyrotron development are discussed in detail.