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In this study, we design and simulate a metal implanted dielectrically modulated tunneling field-effect transistor (MI-DMTFET). In the ambipolar conduction state, the proposed structure works as an efficient sensor for the detection of a wide range of biomolecules. A metal strip (MS) is implanted above the drain-channel junction in the gate dielectric to improve the alignment of band gaps. Therefore, with the help of implanted metal work function engineering, the tunneling barrier gets lowered, which in turn increases the ambipolar current. An optimum metal-strip implant work function of 4.85 eV and a length of 1.5 nm have resulted in significantly improved performance of the proposed device. It has been observed that when the biomolecules with varying dielectric constants and charge densities are captured in the nanogap cavity, the ambipolar current of the biosensor changes, resulting in the detection of the biomolecules. Quantitative and comprehensive analyses of device parameters such as surface potential, electric field, band-to-band tunneling, subthreshold slope, and I ON / I OFF ratio analysis have been performed. Rigorous comparative analyses of key performance-measuring parameters have been performed with a conventional sensor device. It has been found that the proposed device offers maximum sensitivity of 1220 under an ambipolar state at k = 12.

Network Function Virtualization (NFV) offers an alternate method to design, deploy and manage network services. The NFV decouples network functions from the dedicated hardware and moves them to the virtual servers so that they can run in the software. One of the major strengths of the NFV is its ability to dynamically extend or reduce resources allocated to Virtual Network Functions (VNF) as needed and at run-time. There is a need for a comprehensive metering component in the cloud to store and process the metrics/samples for efficient auto-scaling or load-management of the VNF. In this paper, we propose an integrating framework for efficient auto-scaling of VNF using Gnocchi; a time-series database that is integrated within the framework to store, handle and index the time-series data. The objective of this study is to validate the efficacy of employing Gnocchi for auto-scaling of VNF, in terms of aggregated data points, database size, data recovery speed, and memory consumption. The employed methodology is to perform a detailed empirical analysis of the proposed framework by deploying a fully functional cloud to implement NFV architecture using several OpenStack components including Gnocchi. Our results show a significant improvement over the legacy Ceilometer configuration in terms of lower metering storage size, less memory utilization in processing and management of metrics, and reduced time delay in retrieving the monitoring data to evaluate alarms for the auto-scaling of VNF.

Epoxy is considered to be the most popular polymer and is widely used in various engineering applications. However, environmental considerations require natural materials-based epoxy. This necessity results in further utilization of natural materials as a natural reinforcement for different types of composites. Corn cob is an example of a natural material that can be considered as an agricultural waste. The objective of the present work is to improve the economic feasibility of corn cob by converting the original corn cob material into powder to be utilized in reinforcing epoxy-based composites. In the experiment, the corn cob was crushed and ground using a grain miller before it was characterized by scanning electron microscopy (SEM). The corn cob powder was added to the epoxy with different weight fractions (2, 4, 6, 8, 10 wt%). In order to prevent corn cob powder agglomeration and ensure homogeneous distribution of the reinforcement inside the epoxy, the ultrasonic technique and a mechanical stirrer were used. Then, the composite's chemical compositions were evaluated using X-ray diffraction (XRD). The mechanical experiments showed an improvement in the Young's modulus and compressive yield strength of the epoxy composites, increasing corn cob up to 8 wt% by 21.26% and 22.22%, respectively. Furthermore, tribological tests revealed that reinforcing epoxy with 8 wt% corn cob can decrease the coefficient of friction by 35% and increase wear resistance by 4.8%. A finite element model for the frictional process was constructed to identify different contact stresses and evaluate the load-carrying capacity of the epoxy composites. The finite element model showed agreement with the experimental results. An epoxy containing 8 wt% corn cob demonstrated the optimal mechanical and tribological properties. The rubbed surfaces were investigated by SEM to identify the wear mechanism of different composites.

This study is focuses on the investigation of the effect of using TiO 2 short nanofibers as a reinforcement of an Al matrix on the corrosion characteristics of the produced nanocomposites. The TiO 2 ceramic nanofibers used were synthesized via electrospinning by sol-gel process, then calcinated at a high temperature to evaporate the residual polymers. The fabricated nanocomposites contain 0, 1, 3 and 5 wt.% of synthesized ceramic nanofibers (TiO 2 ). Powder mixtures were mixed for 1 h via high-energy ball milling in a vacuum atmosphere before being inductively sintered through a high-frequency induction furnace at 560 °C for 6 min. The microstructure of the fabricated samples was studied by optical microscope and field emission scanning electron microscope (FESEM) before and after corrosion studies. Corrosion behavior of the sintered samples was evaluated by both electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization techniques (PPT) in 3.5% NaCl solution for one hour and 24-h immersion times. The results show that even though the percentage of ceramic nanofibers added negatively control corrosion resistance, it is still possible to increase resistance against corrosion for the fabricated nanocomposite by more than 75% in the longer exposure time periods.

This study explores the possibility of transforming lignocellulose-rich agricultural waste materials into value-added products. Cellulose was extracted from an empty fruit bunch of oil palm and further modified into carboxymethyl cellulose (CMC), a water-soluble cellulose derivative. The CMC was then employed as the polymeric content in fabrication of solid polymer electrolyte (SPE) films incorporated with lithium iodide. To enhance the ionic conductivity of the solid polymer electrolytes, the compositions were optimized with different amounts of glycerol as a plasticizing agent. The chemical and physical effects of plasticizer content on the film composition were studied by Fourier transform infrared (FTIR) and X-ray diffraction (XRD) analysis. FTIR and XRD analysis confirmed the interaction plasticizer with the polymer matrix and the amorphous nature of fabricated SPEs. The highest ionic conductivity of 6.26 × 10 -2 S/cm was obtained with the addition of 25 wt % of glycerol. By fabricating solid polymer electrolytes from oil palm waste-derived cellulose, the sustainability of the materials can be retained while reducing the dependence on fossil fuel-derived materials in electrochemical devices.

In this study, phytochemical assisted nanoparticle synthesis was performed using Muntingia calabura leaf extracts to produce copper oxide nanoparticles (CuO NPs) with interesting morphology. Scanning electron microscope (SEM) and transmission electron microscope (TEM) analysis of the biosynthesized CuO NPs reveal formation of distinct, homogeneous, and uniform sized CuO nanorods structure with thickness and length of around 23 nm and 79 nm, respectively. Based on Fourier-transform infrared (FTIR) analysis, the unique combinations of secondary metabolites such as flavonoid and polyphenols in the plant extract are deduced to be effective capping agents to produce nanoparticles with unique morphologies similar to conventional chemical synthesis. X-ray diffraction (XRD) analysis verified the monoclinical, crystalline structure of the CuO NPs. The phase purity and chemical identity of the product was consolidated via X-Ray photoelectron spectroscopy (XPS) and Raman spectroscopic data which indicate the formation of a single phase CuO without the presence of other impurities. The direct and indirect optical band gap energies of the CuO nanorods were recorded to be 3.65 eV and 1.42 eV.

Industrial and agricultural processes have led to the prevalence of cadmium in the ecosystem. A successive build-up of cadmium in food and drinking water can result in inadvertent consumption of hazardous concentrations. Such environmental contamination of cadmium can pose a substantial threat to human and animal life. In humans, it is known to cause hypertension, cardiovascular diseases, DNA lesions, inhibition of DNA repair protein or disturb the functioning of lung, liver, prostate and kidney. The development of a reliable method for Cd (II) ions detection would reduce the exposure and complement existing conventional methods. In this study, a DNA based electrochemical method is employed for the detection of Cd (II) ions using ethyl green (EG) and multi-walled carbon nanotube (MWCNT). Glassy carbon electrode (GCE)/MWCNT forms the working electrode for differential pulse voltammetry (DPV) analysis for the detection of Cd (II) ions. The dsDNA is immobilized onto the working electrode. The indicator dye EG, preferably binds to ssDNA and its reduction peak current is noticeably less in the presence of dsDNA. The Cd (II) ions after interacting with dsDNA, unwinds the dsDNA to ssDNA, upon which the EG molecules bind to ssDNAs, giving a higher reduction peak current. The difference in the reduction peak currents in the presence and absence of Cd (II) ions is proportional to its concentration. The linear detection range achieved in this method is 2 nM-10.0 nM with a sensitivity of around 5 nA nM -1 and the limit of detection is 2 nM, which is less than the permissible limit of WHO for human exposure. This study considerably broadens the possible application of multi-walled carbon nanotube modified electrodes as biosensors and holds prospects for the detection of other heavy metals in environmental samples., (Copyright © 2021 Elsevier Inc. All rights reserved.)

The direction-of-arrival (DoA) estimation of an acoustic source can be estimated with a uniform linear array using classical techniques such as generalized cross-correlation, beamforming, subspace techniques, etc. However, these methods require a search in the angular space and also have a higher angular error at the end-fire. In this paper, we propose the use of regression techniques to improve the results of DoA estimation at all angles including the end-fire. The proposed methodology employs curve-fitting on the received multi-channel microphone signals, which when applied in tandem with support vector regression (SVR) provides a better estimation of DoA as compared to the conventional techniques and other polynomial regression techniques. A multilevel regression technique is also proposed, which further improves the estimation accuracy at the end-fire. This multilevel regression technique employs the use of linear regression over the results obtained from SVR. The techniques employed here yielded an overall 63% improvement over the classical generalized cross-correlation technique.

Silica nanoparticles are under development for intracellular drug delivery applications but can also have cytotoxic effects including cell membrane damage. In this study, we investigated the interactions of silica nanospheres of different size, surface chemistry and biocoating with membranes of phosphatidylcholine lipids. In liposome leakage assays many, but not all, of these nanoparticles induced dose-dependent dye leakage, indicative of membrane perturbation. It was found that 200 and 500 nm native-silica, aminated and carboxylated nanospheres induce near-total dye release from zwitterionic phosphatidylcholine liposomes at a particle/liposome ratio of ∼1, regardless of their surface chemistry, which we interpret as particle-supported bilayer formation following a global rearrangement of the vesicular membrane. In contrast, 50 nm diameter native-silica nanospheres did not induce total dye leakage below a particle/liposome ratio of ∼8, whereas amination or carboxylation, respectively, strongly reduced or prevented dye release. We postulate that for the smaller nanospheres, strong silica-bilayer interactions are manifested as bilayer engulfment of membrane-adsorbed particles, with localized lipid depletion eventually leading to collapse of the vesicular membrane. Protein coating of the particles considerably reduced dye leakage and lipid bilayer coating prevented dye release all together, while the inclusion of 33% anionic lipids in the liposomes reduced dye leakage for both native-silica and aminated surfaces. These results, which are compared with the effect of polystyrene nanoparticles and other engineered nanomaterials on lipid bilayers, and which are discussed in relation to nanosilica-induced cell membrane damage and cytotoxicity, indicate that a native-silica nanoparticle surface chemistry is a particularly strong membrane interaction motif.