Optimized performance of nickel in crystal-layered arrangement of NiFe2O4/rGO hybrid for high-performance oxygen evolution reaction

To rescue the future from the global energy crisis and to ensure it with clean and economical hydrogen energy, it is an urgency to develop an efficient OER catalyst, which intensely sluggish the kinetic process of hydrogen production. Herein, we have precisely synthesized an efficient, stable, earth-abundant metal-based NiFe₂O₄/rGO hybrid OER electrocatalysts by a simple solvothermal method. The measurements including XRD, FTIR, XPS, EDS, SEM, and TEM revealed the prominent structural integrity of catalyst with crystal-layered structure. The rich oxidation chemistry of transition metals and substantially active carbon substrate allows tuning of their electronic properties concerning their concentration, composition, and morphology. The effect of different Ni wt.% (0%, 2%, 4%, and 6%) on the morphology of hybrid as well as on electrochemical performance investigated. The protocols like overpotential required to achieve a current density of 10 mA/cm², Tafel slope, ECSA, RF, EIS, stability was utilized to examine the overall abilities of electrocatalyst in alkaline 1 M KOH solution. The optimized NiFe₂O₄/rGO hybrid with 2 wt % Ni exhibited the excellent OER performance, which delivers a current density of 10 mA/cm² at an overpotential of only 302 mV with a small Tafel slope of 63 mV/dec. The high activity of the catalyst is attributed to the synergistic effect of the crystal-layered structure as well as rapid mass-charge transfer. Such, rational design concept of anchoring non-precious metal on carbon in a controlled manner, offering splendid flexibility to tailor electrochemical OER performance. The optimized variations in metal concentration and morphologies, providing a promising route to develop a cost-effective catalyst for advanced energy conversion applications.     

Production of hydrogen energy through biomass (waste wood) gasification

Biomass gasification, conversion of solid carbonaceous fuel into combustible gas by partial combustion, is a prominent technology for the production of hydrogen from biomass. The concentration of hydrogen in the gas generated from gasification depends mainly upon moisture content, type and composition of biomass, operating conditions and configuration of the biomass gasifier. The potential of production of hydrogen from wood waste by applying downdraft gasification technology is investigated. An experimental study is carried out using an Imbert downdraft biomass gasifier covering a wide range of operating parameters. The producer gas generated in the downdraft gasifier is analyzed using a gas chromatograph (NUCON 5765) with thermal conductivity detector (TCD). The effects of air flow rate and moisture content on the quality of producer gas are studied by performing experiments. The performance of the biomass gasifier is evaluated in terms of equivalence ratio, composition of producer gas, and rate of hydrogen production.     

Electrodeposited MnO2 as electrocatalyst for carbohydrate oxidation

The electrochemical oxidation of dextrose, fructose and sorbitol under galvanostatic conditions was carried out using both anodically and cathodically deposited MnO₂ layers on platinum and carbon. The coated electrodes showed better electrocatalytic activity for the oxidation of carbohydrates than the bare substrates. Catalytic participation of some higher valent states of manganese in electron transfer relay is speculated, which finds support in the chronopotentiogram and the observed pH-dependence of the electrochemical parameters. The current potential plots showed Tafel behaviour for the MnO₂/Pt electrode. The Tafel slopes were found to be relatively high, indicating kinetic complications. The observed unusual negative values of electrochemical reaction order on MnO₂/Pt electrode were accounted for by considering slow desorption of oxidation products from the electrode surface. The adsorption isotherms of dextrose and fructose on MnO₂ were determined. The current efficiencies of oxidation of carbonyl and hydroxyl groups were found to be and % respectively. SEM pictures showed the cathodically deposited MnO₂ on carbon to be more fine-grained and smoother than the corresponding anodic deposits.     

Magneto-dielectric coupling in SmFeO3: A study on anomalous dielectric,conductivity, impedance at spin reorientation temperature

The SmFeO₃, a family member of rare earth orthoferrite, is a potential candidate for future industrial (spintronics) applications due to highest spin reorientation temperature (453-483?K) among other RFeO₃ members. There are ambiguous reports on high permittivity behavior of SmFeO₃. In this report, the origin of high permittivity in SmFeO₃ is attributed to microstructure rather than inherent material property of material. Ceramic pellets of SmFeO₃ were prepared in two different routes i. e. solid-state route and nano-combustion synthesis route. The dielectric and conductivity properties of these pellets were studied as function of frequency as well as temperature. Impedance spectroscopy was used to study the dielectric and resistive behavior of SmFeO₃. The central findings of this work are; (i) the high dielectric constant of SmFeO₃ is not an inherent material property instead strongly dependent on microstructure and preparation conditions and (ii) the anomalous behavior in dielectric, conductivity and impedance observed at spin reorientation temperature regime of SmFeO₃ is attributed to strong magneto dielectric coupling in SmFeO₃.     

FakeBERT: Fake news detection in social media with a BERT-based deep learning approach

In the modern era of computing, the news ecosystem has transformed from old traditional print media to social media outlets. Social media platforms allow us to consume news much faster, with less restricted editing results in the spread of fake news at an incredible pace and scale. In recent researches, many useful methods for fake news detection employ sequential neural networks to encode news content and social context-level information where the text sequence was analyzed in a unidirectional way. Therefore, a bidirectional training approach is a priority for modelling the relevant information of fake news that is capable of improving the classification performance with the ability to capture semantic and long-distance dependencies in sentences. In this paper, we propose a BERT-based (Bidirectional Encoder Representations from Transformers) deep learning approach (FakeBERT) by combining different parallel blocks of the single-layer deep Convolutional Neural Network (CNN) having different kernel sizes and filters with the BERT. Such a combination is useful to handle ambiguity, which is the greatest challenge to natural language understanding. Classification results demonstrate that our proposed model (FakeBERT) outperforms the existing models with an accuracy of 98.90%.

     

EchoFakeD: improving fake news detection in social media with an efficient deep neural network

Neural Computing and Applications - The increasing popularity of social media platforms has simplified the sharing of news articles that have led to the explosion in fake news. With the emergence...     

A blend of first-principles and kinetic lattice Monte Carlo computation to optimize samarium-doped ceria

Solid oxide fuel cells (SOFCs) have been acknowledged as a possible future source for clean and efficient electric power generation. One of the most important goals in the SOFCs research is to decrease the operating temperature, which in turn will improve the stability and decrease the cost of various components enabling its widespread utilization. For realizing the aforementioned goal, it is imperative to identify suitable electrolyte materials that show enhanced conductivity in the intermediate temperature range (773–1,073 K). Sm-doped ceria (SDC) is considered a promising candidate for use as an electrolyte material for SOFC operation in intermediate temperature range due to the high oxygen ion conductivity. In this article, we present a theoretical investigation using first-principles and kinetic lattice Monte Carlo (KLMC) computations to highlight the trends in oxygen ion conductivity as a function of dopant content and temperature in SDC. Using first-principles calculations, oxygen vacancy formation and migration were examined at first, second, and third nearest neighbor positions to a Sm ion. The activation energies for oxygen vacancy migration along various pathways in SDC computed using first-principles were used as input to the KLMC model to study vacancy mediated diffusion. SDC with 20 % mole fraction of dopant content yields the maximum conductivity, which is in very good agreement with experimentally identified compositions. Rationale for increase in conductivity as a function of increase in dopant content and subsequent decrease in conductivity at higher dopant fractions in SDC is presented. This combined methodology of first-principles and KLMC computations is a useful tool for the design and identification of various ceria-based electrolyte materials used in SOFCs.     

Liquid-phase exfoliation of WSe2 nanosheets for ITO/WSe2 photodetector

Journal of Materials Science: Materials in Electronics - The potential application of atomically thin two-dimensional (2D)-layered WSe2 in future wearable electronics has sparked a lot of interest....