In situ preparation of N doped orthorhombic Nb2O5 nanoplates /rGO composites for photocatalytic hydrogen generation under sunlight

The synthesis of nitrogen doped orthorhombic niobium oxide nanoplates/reduced graphene oxide composites (NNb₂O₅/rGO) and their photocatalytic activity towards hydrogen generation from water and H₂S under natural sunlight has been demonstrated, uniquely. Nanostructured NNb₂O₅/rGO is synthesized by in situ wet chemical method using urea as a source of nitrogen and optimized by varying percentage of graphene oxide (GO). X?ray diffraction (XRD) study reveals that NNb₂O₅ have orthorhombic crystal structure with crystalline size, 35 nm. Further, X?ray photoelectron spectroscopy (XPS) confirm the presence of nitrogen and rGO in NNb₂O₅/rGO nanocomposite. Morphological features of (NNb₂O₅/rGO) were examined by FE?SEM and FE?TEM showed Nb₂O₅ nanoplates of diameter 25–40 nm anchored on 2D rGO. Diffuse reflectance spectra depicts the extended absorbance in the visible region with band gap of 2.2 eV. Considering the band gap in the visible region, the photocatalytic hydrogen generation from water and H₂S has been performed. The 1 wt % rGO hybridized NNb₂O₅ (S2) exhibited superior photocatalytic hydrogen generation (537 μmol/h) from water and (1385 μmol/h) from H₂S under sunlight. The improved photocatalytic activity is attributed due to an extended absorbance in the visible region, modified electronic structure upon doping and formation of well defined NNb₂O₅/rGO interface, provides large surface area, accelerates the supression of electron and hole pairs recombination rate. In our opinion, this works may provides facile route for energy efficient and economic approach for fabrication of NNb₂O₅/rGO nanocomposites as a visible light active photocatalyst.     

Fault section estimation in power system using Hebb’s rule and continuous genetic algorithm

In this paper a new approach for fault section estimation (FSE) in electrical power system is presented. We propose a procedure to obtain objective function (required for fault section estimation) using the Hebb’s learning rule. The continuous genetic algorithm (CGA) optimization method is then employed to estimate the fault section making use of the objective function. The Hebb’s learning law used in this paper gives, linear algebraic equations, to represent the targets in terms of the status of relays and circuit breakers (CBs). This gives a simple objective function, which leads to reduction in time required by the CGA to identify fault section. The CGA gives an advantage of requiring less storage than binary genetic algorithm (GA). Also the CGA is inherently faster than binary GA.The proposed approach is tested on various systems, and is found to give correct results in all cases. Simulation results for two illustrations have been presented in this paper. The results show that the proposed approach can find the solution efficiently even in case of multiple faults or in case of failure of relays/circuit breakers. A comparison with artificial neural network (ANN) approach is also presented.     

The State of the Art in Visualizing Dynamic Multivariate Networks

Most real-world networks are both dynamic and multivariate in nature, meaning that the network is associated with various attributes and both the network structure and attributes evolve over time. Visualizing dynamic multivariate networks is of great significance to the visualization community because of their wide applications across multiple domains. However, it remains challenging because the techniques should focus on representing the network structure, attributes and their evolution concurrently. Many real-world network analysis tasks require the concurrent usage of the three aspects of the dynamic multivariate networks. In this paper, we analyze current techniques and present a taxonomy to classify the existing visualization techniques based on three aspects: temporal encoding, topology encoding, and attribute encoding. Finally, we survey application areas and evaluation methods; and discuss challenges for future research.     

Road Map for In Situ Grown Binder-Free MOFs and Their Derivatives as Freestanding Electrodes for Supercapacitors

Among several electrocatalysts for energy storage purposes including supercapacitors, metal–organic frameworks (MOFs), and their derivatives have spurred wide spread interest owing to their structural merits, multifariousness with tailor-made functionalities and tunable pore sizes. The electrochemical performance of supercapacitors can be further enhanced using in situ grown MOFs and their derivatives, eliminating the role of insulating binders whose “dead mass” contribution hampers the device capability otherwise. The expulsion of binders not only ensures better adhesion of catalyst material with the current collector but also facilitates the transport of electron and electrolyte ions and remedy cycle performance deterioration with better chemical stability. This review systematically summarizes different kinds of metal–ligand combinations for in situ grown MOFs and derivatives, preparation techniques, modification strategies, properties, and charge transport mechanisms as freestanding electrode materials in determining the performance of supercapacitors. In the end, the review also highlights potential promises, challenges, and state-of-the-art advancement in the rational design of electrodes to overcome the bottlenecks and to improve the capability of MOFs in energy storage applications.     

ChemoGraph: Interactive Visual Exploration of the Chemical Space

Exploratory analysis of the chemical space is an important task in the field of cheminformatics. For example, in drug discovery research, chemists investigate sets of thousands of chemical compounds in order to identify novel yet structurally similar synthetic compounds to replace natural products. Manually exploring the chemical space inhabited by all possible molecules and chemical compounds is impractical, and therefore presents a challenge. To fill this gap, we present ChemoGraph, a novel visual analytics technique for interactively exploring related chemicals. In ChemoGraph, we formalize a chemical space as a hypergraph and apply novel machine learning models to compute related chemical compounds. It uses a database to find related compounds from a known space and a machine learning model to generate new ones, which helps enlarge the known space. Moreover, ChemoGraph highlights interactive features that support users in viewing, comparing, and organizing computationally identified related chemicals. With a drug discovery usage scenario and initial expert feedback from a case study, we demonstrate the usefulness of ChemoGraph.     

Recent Progress on Sensitivity Analysis of Schottky Field Effect transistor Based Biosensors

Silicon - In this review, we explored the modern development of schottky field effect transistor (SK FET) structures and the improvement of sensitivity of nanowire sensors using dielectric...     

Differential transform method for couple stress fluid flow in a channel with a porous base

The paper presents an application of the semi-analytical differential transform method (DTM) to thermofluidics of generalized Couette flow of couple stress fluid inside a composite channel. The channel comprises a fluid-saturated porous layer and a clear fluid region. The flow is caused by a uniformly favorable pressure gradient applied at the mouth of the channel and the shear of the moving upper wall of the channel. The matching conditions are taken such that the velocity, temperature, and respective gradients are continuous at a clear fluid–porous medium. The problem is novel because this is the first mathematical endeavor to treat fluidic systems involving composite channels. Besides the mathematical rigor, it presents an analysis of thermodynamic irreversibility and other quantities of interest, viz, skin friction and Nusselt number. The results derived from DTM are compared with those obtained by exact solution showing excellent agreement. The plots and tables are drawn and discussed.     

Dual Metal Gate Dielectric Engineered Dopant Segregated Schottky Barrier MOSFET With Reduction in Ambipolar Current

Silicon - In this paper, to solve the problem of higher ambipolar leakage current (Iambipolar) of Dielectric Engineered (DE) Dopant Segregated (DG) Schottky Barrier (SB) MOSFET (DE DS SBMOS), we...