Comparative analysis of memristor models and memories design

The advent of the memristor breaks the scaling limitations of MOS technology and prevails over emerging semiconductor devices.In this paper,various memristor models including behaviour,spice,and experimental are investigated and compared with the memristor's characteristic equations and fingerprints.It has brought to light that most memristor models need a window function to resolve boundary conditions.Various challenges of availed window functions are discussed with matlab's simulated results.Biolek's window is a most acceptable window function for the memristor,since it limits boundaries growth as well as sticking of states at boundaries.Simmons tunnel model of a memristor is the most accepted model of a memristor till now.The memristor is exploited very frequently in memory designing and became a prominent candidate for futuristic memories.Here,several memory structures utilizing the memristor are discussed.It is seen that a memristor-transistor hybrid memory cell has fast read/write and low power operations.Whereas,a 1T1R structure provides very simple,nanoscale,and non-volatile memory that has capabilities to replace conventional Flash memories.Moreover,the memristor is frequently used in SRAM cell structures to make them have non-volatile memory.This paper contributes various aspects and recent developments in memristor based circuits,which can enhance the ongoing requirements of modem designing criterion.     

ConvNet frameworks for multi-modal fake news detection

An upsurge of false information revolves around the internet. Social media and websites are flooded with unverified news posts. These posts are comprised of text, images, audio, and videos. There is a requirement for a system that detects fake content in multiple data modalities. We have seen a considerable amount of research on classification techniques for textual fake news detection, while frameworks dedicated to visual fake news detection are very few. We explored the state-of-the-art methods using deep networks such as CNNs and RNNs for multi-modal online information credibility analysis. They show rapid improvement in classification tasks without requiring pre-processing. To aid the ongoing research over fake news detection using CNN models, we build textual and visual modules to analyze their performances over multi-modal datasets. We exploit latent features present inside text and images using layers of convolutions. We see how well these convolutional neural networks perform classification when provided with only latent features and analyze what type of images are needed to be fed to perform efficient fake news detection. We propose a multi-modal Coupled ConvNet architecture that fuses both the data modules and efficiently classifies online news depending on its textual and visual content. We thence offer a comparative analysis of the results of all the models utilized over three datasets. The proposed architecture outperforms various state-of-the-art methods for fake news detection with considerably high accuracies.

     

Optimal Design of Barrage Profile on Anisotropic Soil Using Multi‐Objective Optimization Approach

Weirs and barrages are costly hydraulic diversion structures; therefore, any attempt to improve their design is a worthy contribution. Diversion structures, such as weir or barrage, may be designed on permeable formations considering homogenous soil properties. But in reality, soil properties are hardly homogeneous. In this paper, an approach is described to determine an economically efficient barrage profile by considering soil’s anisotropic behaviour. Hydraulic conductivity is considered to be an anisotropic soil property. An optimization-based methodology is developed to obtain the optimal barrage profile. The minimization of the material cost and minimization of the exit gradient is considered for multi-objective formulation. The multi-objective formulation is solved using NSGA-II, and a Pareto optimal front is obtained for different degrees of anisotropy. The flow interaction under a diversion structure in anisotropic soil is incorporated using the Modified Lane theory and is embedded in optimization formulation. The developed methodology is illustrated with a barrage profile as a hydraulic structure. A parametric study is carried out to study the effects of varying barrage design elements on the barrage’s optimum material cost.

     

Substrate/film interactions in thick films of YBaCuO on alumina

The substrate-film interaction in thick films (>10m) of YBa₂Cu₃O_7–x on alumina processed under normal conditions is investigated using electron-probe microanalysis. The formation of a mixture of barium aluminate and alumina over a thickness of about 2m in the interfacial region is established quantitatively using compositional mapping and point-count analysis across the substrate-film interface. Diffusion of aluminium into the film over severalm beyond the reaction layer is also observed. The variation of oxygen composition across the interface has been mapped. Leaching of oxygen from the 1-2-3 phase in the bulk is suggested as the reason for the observed decrease inT _c(0) and increase in T _c in films of YBa₂Cu₃O_7–x on alumina.     

Benevolent behavior ofKleinia grandifloraleaf extract as a green corrosion inhibitor for mild steel in sulfuric acid solution

The ethanolic extract of Kleinia grandiflora leaves was characterized and tested for its potential anticorrosion properties on mild steel in 1 M H2SO4 medium using mass-loss analysis, potentiodynamic polarization measurements, electrochemical impedance spectroscopy, Fourier-transform infrared spectroscopy, scanning electron microscopy, UV–visible spectroscopy, and X-ray diffraction analysis. The effect of temperature on the corrosion behavior of mild steel was studied in the range of 308 to 328 K. The inhibition efficiency was observed to increase with increasing concentration of the extract. Polarization curves revealed that the Kleinia grandiflora leaf extract is a mixed inhibitor. Impedance diagrams revealed that an increase of Kleinia grandiflora leaf extract concentration increased the charge transfer resistance and decreased the double-layer capacitance. The adsorption process obeys Langmuir＇s model, with a standard free energy of adsorption（ΔGads） of-18.62 k J/mol. The obtained results indicate that the Kleinia grandiflora leaf extract can serve as an effective inhibitor for the corrosion of mild steel in a sulfuric acid medium.     

A Green and Facile Way to Prepare Granadilla‐Like Silicon‐Based Anode Materials for Li‐Ion Batteries

A yolk‐shell‐structured carbon@void@silicon (CVS) anode material in which a void space is created between the inside silicon nanoparticle and the outer carbon shell is considered as a promising candidate for Li‐ion cells. Untill now, all the previous yolk‐shell composites were fabricated through a templating method, wherein the SiO₂ layer acts as a sacrificial layer and creates a void by a selective etching method using toxic hydrofluoric acid. However, this method is complex and toxic. Here, a green and facile synthesis of granadilla‐like outer carbon coating encapsulated silicon/carbon microspheres which are composed of interconnected carbon framework supported CVS nanobeads is reported. The silicon granadillas are prepared via a modified templating method in which calcium carbonate was selected as a sacrificial layer and acetylene as a carbon precursor. Therefore, the void space inside and among these CVS nanobeads can be formed by removing CaCO₃ with diluted hydrochloric acid. As prepared, silicon granadillas having 30% silicon content deliver a reversible capacity of around 1100 mAh g^?1 at a current density of 250 mA g^?1 after 200 cycles. Besides, this composite exhibits an excellent rate performance of about 830 and 700 mAh g^?1 at the current densities of 1000 and 2000 mA g^?1, respectively.     

Modeling and simulation of carbon nanotube field effect transistor and its circuit application

The carbon nanotube field effect transistor (CNTFET) is modelled for circuit application. The model is based on the transport mechanism and it directly relates the transport mechanism with the chirality. Also, it does not consider self consistent equations and thus is used to develop the HSPICE compatible circuit model. For validation of the model, it is applied to the top gate CNTFET structure and the MATLAB simulation results are compared with the simulations of a similar structure created in NanoTCAD ViDES. For demonstrating the circuit compatibility of the model, two circuits viz. inverter and SRAM are designed and simulated in HSPICE. Finally, SRAM performance metrics are compared with those of device simulations from NanoTCAD ViDES.     

Compact model for ballistic single wall CNTFET under quantum capacitance limit

This paper proposes a compact model for carbon nanotube field effect transistor (CNTFET) based on surface potential and conduction band minima. The proposed model relates the I–V characteristics to chirality under quantum capacitance limit. C–V characteristics have been efficiently modelled for different capacitance models which are used to find the relationship between CNT surface potential and gate voltage. The role of different capacitances is discussed and it has been found that the proposed circuit compact model strictly follows quantum capacitance limit. The proposed model is efficiently designed for circuit simulations as it denies self-consistent numerical simulation. Furthermore, this compact model is compared with experimental results. The model has been used to simulate an inverter using HSPICE.     

Improving combustion characteristics of diesel and biodiesel droplets by graphite oxide addition for diesel engine applications

Graphite oxide (GO) is an important member of the graphene family of carbon nanomaterials with remarkable physical, chemical, and thermal properties. We conducted an experimental investigation on the combustion characteristics of diesel and biodiesel droplets dosed with 0.1% GO. The fuels were tested by a single droplet combustion experiment in which the temporal variation in the burning behavior of a suspended droplet was captured using a high‐speed camera. Numerical analysis of the combustion data suggests that the addition of GO in both fuels resulted in shortened ignition delay (by up to 38.2%), increased burn‐rate constant (by up to 29.4%), lowered peak temperature (by up to 7.8%), and shortened burning period (by up to 11.6%). To illustrate, the burn‐rate constant increased from 0.68 to 0.88 mm²/second, and the burning period reduced from 2.7 to 2.2 seconds when GO was dosed in diesel. By contrast, the ignition delay and peak temperature both decreased from 1.6 to 1.4 seconds and 659 to 611 K, respectively, when GO was added in biodiesel. Our results suggest that the fuel additive–induced benefits could effectively reduce emissions and improve fuel consumption for diesel engine applications.