Influence of Sn content on the electrocatalytic activity of NiSn alloy nanoparticles-incorporated carbon nanofibers toward methanol oxidation

Improvement of the electrocatalytic activity of nickel toward methanol oxidation can be conducted by exploiting the synergetic influence of a co-catalyst and/or utilizing a proper support. In this study, utilizing tin as a co-catalyst and supporting on carbon nanofibers are proposed to enhance methanol oxidation in the alkaline media. Typically, NiSn nanoparticles alloy-incorporated carbon nanofibers could be prepared by calcination of electrospun nanofibers composed of poly (vinyl alcohol), nickel acetate tetrahydrate and tin chloride under argon atmosphere at a high temperature. The influence of the co-catalyst content and the calcination temperature on the morphology, composition and electrocatalytic activity of the proposed nanofibers was investigated. Smooth electrospun nanofibers can be prepared regardless the tin chloride content up to 35 wt%, and the calcination process did not distinctly affect the nanofibrous morphology. Mostly, Ni₃Sn and Ni₃Sn₂ nanoparticles-incorporated amorphous carbon nanofibers were obtained at all the utilized calcination temperatures (700, 850 and 1000 °C) and examined SnCl₂ contents. However, at 10 wt% SnCl₂ content and 850 °C calcination temperature, single metallic compound (Ni₃Sn₂)-incorporated carbon nanofibers were synthesized. Electrochemical measurements indicated that the electrocatalytic activity depends strongly on the tin content as well as the calcination temperature. The nanofibers obtained from electrospun solution containing 10 wt% SnCl₂ and calcined at 850 °C showed very good performance compared to the other formulations. Typically, the corresponding onset potential of the methanol oxidation reaction using these nanofibers catalyst is 315 mV (vs. Ag/AgCl) while it was 405 mV for the nanofibers obtained from electrospun solution containing 0, 5, 15, 25 and 35 wt% SnCl₂. Moreover, the best nanofibers reveal the highest current density. Kinetic study indicated that the corresponding activation energy is 15.6 kJ/mol.     

A flexible nickel phthalocyanine resistive random access memory with multi-level data storage capability

Metal phthalocyanine is considered one of the most promising candidates for the design and fabrication of flexible resistive random access memory(RRAM)devices due to its intrinsic flexibility and excel-lent functionality.However,performance degradation and the lack of multi-level capability,which can directly expand the storage capacity in one memory cell without sacrificing additional layout area,are the primary obstacles to the use of metal phthalocyanine RRAMs in information storage.Here,a flex-ible RRAM with pristine nickel phthalocyanine(NiPc)as the resistive layer is reported for multi-level data storage.Due to its high trap-concentration,the charge transport behavior of the device agrees well with classical space charge limited conduction controlled by traps,leading to an excellent performance,including a high on-off current ratio of 107,a long-term retention of 106 s,a reproducible endurance over 6000 cycles,long-term flexibility at a bending strain of 0.6％,a write speed of 50 ns under sequential bias pulses and the capability of multi-level data storage with reliable retention and uniformity.     

Evaluating the Impact of Prediction Techniques: Software Reliability Perspective

Maintaining software reliability is the key idea for conducting quality research. This can be done by having less complex applications. While developers and other experts have made significant efforts in this context, the level of reliability is not the same as it should be. Therefore, further research into the most detailed mechanisms for evaluating and increasing software reliability is essential. A significant aspect of growing the degree of reliable applications is the quantitative assessment of reliability. There are multiple statistical as well as soft computing methods available in literature for predicting reliability of software. However, none of these mechanisms are useful for all kinds of failure datasets and applications. Hence finding the most optimal model for reliability prediction is an important concern. This paper suggests a novel method to substantially pick the best model of reliability prediction. This method is the combination of analytic hierarchy method (AHP), hesitant fuzzy (HF) sets and technique for order of preference by similarity to ideal solution (TOPSIS). In addition, using the different iterations of the process, procedural sensitivity was also performed to validate the findings. The findings of the software reliability prediction models prioritization will help the developers to estimate reliability prediction based on the software type.     

Ozone Depletion Identification in Stratosphere Through Faster Region-Based Convolutional Neural Network

The concept of classification through deep learning is to build a model that skillfully separates closely-related images dataset into different classes because of diminutive but continuous variations that took place in physical systems over time and effect substantially. This study has made ozone depletion identification through classification using Faster Region-Based Convolutional Neural Network (F-RCNN). The main advantage of F-RCNN is to accumulate the bounding boxes on images to differentiate the depleted and non-depleted regions. Furthermore, image classification’s primary goal is to accurately predict each minutely varied case’s targeted classes in the dataset based on ozone saturation. The permanent changes in climate are of serious concern. The leading causes beyond these destructive variations are ozone layer depletion, greenhouse gas release, deforestation, pollution, water resources contamination, and UV radiation. This research focuses on the prediction by identifying the ozone layer depletion because it causes many health issues, e.g., skin cancer, damage to marine life, crops damage, and impacts on living being’s immune systems. We have tried to classify the ozone images dataset into two major classes, depleted and non-depleted regions, to extract the required persuading features through F-RCNN. Furthermore, CNN has been used for feature extraction in the existing literature, and those extricated diverse RoIs are passed on to the CNN for grouping purposes. It is difficult to manage and differentiate those RoIs after grouping that negatively affects the gathered results. The classification outcomes through F-RCNN approach are proficient and demonstrate that general accuracy lies between 91% to 93% in identifying climate variation through ozone concentration classification, whether the region in the image under consideration is depleted or non-depleted. Our proposed model presented 93% accuracy, and it outperforms the prevailing techniques.     

Effect of terbium doping in bismuth ferrite nanoparticles for the degradation of organic pollutant under sunlight irradiation

Journal of Materials Science: Materials in Electronics - Herein, we have studied the influence of terbium (Tb) doping in bismuth ferrite (BiFeO3/BFO) nanoparticles towards its photocatalytic...     

A novel efficient mixed formulation for strain-gradient models

Various finite elements based on mixed formulations have been proposed for the solution of boundary value problems involving strain-gradient models. The relevant literature, however, does not provide details on some important theoretical aspects of these elements. In this work, we first present the existing elements within a novel, single mathematical framework, identifying some theoretical issues common to all of them that affect their robustness and numerical efficiency. We then proceed to develop a new family of mixed elements that addresses these issues while being simpler and computationally cheaper. The behavior of the new elements is further demonstrated through two numerical examples.     

Silicon nanotube field effect transistor with core-shell gate stacks for enhanced high-performance operation and area scaling benefits

We introduce the concept of a silicon nanotube field effect transistor whose unique core-shell gate stacks help achieve full volume inversion by giving a surge in minority carrier concentration in the near vicinity of the ultrathin channel and at the same time rapid roll-off at the source and drain junctions constituting velocity saturation-induced higher drive current-enhanced high performance per device with efficient real estate consumption. The core-shell gate stacks also provide superior short channel effects control than classical planar metal oxide semiconductor field effect transistor (MOSFET) and gate-all-around nanowire FET. The proposed device offers the true potential to be an ideal blend for quantum ballistic transport study of device property control by bottom-up approach and high-density integration compatibility using top-down state-of-the-art complementary metal oxide semiconductor flow.     

Home Energy Management System Using NILM and Low-Cost HAN

Home energy management systems(HEMs) are used to provide comfortable life for consumers as well as to save energy. An essential component of HEMs is a home area network(HAN) that is used to remotely control the electric devices at homes and buildings. Although HAN prices have dropped in recent years but they are still expensive enough to prohibit a mass scale deployments. In this paper, a very low cost alternative to the expensive HANs is presented. We have applied a combination of non-intrusive load monitoring(NILM) and very low cost one-way HAN to develop a HEM. By using NILM and machine learning algorithms we find the status of devices and their energy consumption from a central meter and communicate with devices through the one-way HAN. The evaluations show that the proposed machine learning algorithm for NILM achieves up to 99% accuracy in certain cases. On the other hand our radio frequency(RF)-based one-way HAN achieves a range of 80 feet in all settings.     

Home Energy Management System Using NILM and Low-Cost HAN

Home energy management systems (HEMs) are used to provide comfortable life for consumers as well as to save energy. An essential component of HEMs is a home area network (HAN) that is used to remotely control the electric devices at homes and buildings. Although HAN prices have dropped in recent years but they are still expensive enough to prohibit a mass scale deployment. In this paper, a very low cost alternative to the expensive HANs is presented. We have applied a combination of non-intrusive load monitoring (NILM) and very low cost one-way HAN to develop a HEM. By using NILM and machine learning algorithms we find the status of devices and their energy consumption from a central meter and communicate with devices through the one-way HAN. The evaluations show that the proposed machine learning algorithm for NILM achieves up to 99% accuracy in certain cases. On the other hand our radio frequency (RF)-based one-way HAN achieves a range of 80 feet in all settings.