Piezoelectric properties of PVDF-conjugated polymer nanofibers

This study presents the development and characterization of PVDF-conjugated polymer nanofiber-based systems. Five different conducting polymers (CPs) were synthesized successfully and used to create the nanofiber systems. The CPs used are polyaniline (PANI), polypyrrole (PPY), polyindole (PIN), polyanthranilic acid (PANA), and polycarbazole (PCZ). Nanofiber systems were produced utilizing the Forcespinning® technique. The nanofiber systems were developed by mechanical stretching. No electrical field or post-process poling was used in the nanofiber systems. The morphology, structure, electrochemical and piezoelectric performance was characterized. All of the nanofiber PVDF/CP systems displayed higher piezoelectric performance than the fine fiber PVDF systems. The PVDF/PPY nanofiber system displays the highest piezoelectric performance of 15.56 V. The piezoelectric performance of the PVDF/CP nanofiber systems favors potential for an attractive source of energy where highly flexible membranes could be used in power actuators, sensors and portable, and wireless devices to mention some.     

A Novel Self-Organizing Approach to Automatic Traffic Light Management System for Road Traffic Network

Wireless Personal Communications - Traffic network is basically a “network of networks” consisting of mainly two types of networks: road network and a travel network. Due to drastic...     

Design,Synthesis, and Self-Assembly Studies of a Suite of Monodisperse,Facially Amphiphilic,Protein–Dendron Conjugates

The custom design of protein–dendron amphiphilic macromolecules is at the forefront of macromolecular engineering. Macromolecules with this architecture are very interesting because of their ability to self-assemble into various biomimetic nanoscopic structures. However, to date, there are no reports on this concept due to technical challenges associated with the chemical synthesis. Towards that end, herein, a new chemical methodology for the modular synthesis of a suite of monodisperse, facially amphiphilic, protein–dendron bioconjugates is reported. Benzyl ether dendrons of different generations (G1–G4) are coupled to monodisperse cetyl ethylene glycol to form macromolecular amphiphilic activity-based probes (AABPs) with a single protein reactive functionality. Micelle-assisted protein labeling technology is utilized for site-specific conjugation of macromolecular AABPs to globular proteins to make monodisperse, facially amphiphilic, protein–dendron bioconjugates. These biohybrid conjugates have the ability to self-assemble into supramolecular protein nanoassemblies. Self-assembly is primarily mediated by strong hydrophobic interactions of the benzyl ether dendron domain. The size, surface charge, and oligomeric state of protein nanoassemblies could be systematically tuned by choosing an appropriate dendron or protein of interest. This chemical method discloses a new way to custom-make monodisperse, facially amphiphilic, protein–dendron bioconjugates.     

V2O5/RGO/Pt nanocomposite on oxytetracycline degradation and pharmaceutical effluent detoxification

The cover image is based on the Research Article V₂O₅/RGO/Pt nanocomposite on oxytetracycline degradation and pharmaceutical effluent detoxification by Mohan, H et al., DOI: 10.1002/jctb.6238 .

     

Preparation of nanocrystalline nickel oxide from nickel hydroxide using spark plasma sintering and inverse Hall-Petch related densification

Nanocrystalline nickel oxide (NiO) was prepared from nickel hydroxide by Spark plasma sintering (SPS) and the mechanisms involved in the densification of NiO were studied. Reverse precipitated nickel hydroxide powders were SPS processed at 400, 600 and 700?°C with 70?MPa pressure. Pure NiO with 12?nm crystallite size formed after 400?°C sintering process. However NiO grains had grown to 18 and 38?nm after 600 and 700?°C sintering respectively. NiO pellets prepared using 600 and 700?°C SPS sintering schedules had relative densities of 83% and 94% respectively. Two displacement rate regimes were observed during densification of NiO in both 600 and 700?°C sintering processes. Decomposition of nickel hydroxide and particle sliding of NiO led to first displacement rate maximum while inverse Hall-Petch based plastic deformation facilitated densification during the constant second displacement rate regime. No densification occurred during sintering holding times indicating the limited role that diffusion played during densification.     

Performance Analysis of Pulse Generators on Residual Stress of Machined Silicon Steel Using the EDM Process

The cracks in the workpiece specimens can reduce the fatigue life of any machine components. Since the residual stress has a considerable amount of influence on determining crack formation over the machined surface, it is very essential to analyze the residual stress developed in any machining process. However, it is a very tedious process to compute the residual stress over the machined surface. In the present study, an endeavor has been made to measure and analyze the residual stress of machined silicon steel as a workpiece using the EDM process with different energy distribution. The nano-indentation method was used to compute the residual stress produced over the machined surface. From the experimental results, it was found that the uniform energy distribution has produced higher compressive residual stress owing to the tiny and uniform spark energy distribution. It has also been observed that the tool electrode has a considerable amount of influence on determining development of residual stress in the EDM process.     

Tuning of ferrimagnetic nature and hyperfine interaction of Ni2+ doped cobalt ferrite nanoparticles for power transformer applications

Ferrites may contain single domain particles which gets converted into super-paramagnetic state near critical size. To explore the existence of these characteristic feature of ferrites, we have performed magnetization(M-H loop) and Mössbauer spectroscopic studies of Ni²⁺ substitution effect in Co_1-xNi_xFe₂O₄ (where x?=?0, 0.25, 0.5, 0.75 and 1) nanoparticles were fabricated by solution combustion route using mixture of carbamide and glucose as fuels for the first time. As prepared samples exhibit spinel cubic structure with lattice parameters which decreases linearly with increase in Ni²⁺ concentration. The M-H loops reveals that saturation magnetization(M_s), coercive field(H_c) remanence magnetization(M_r) and magnetron number(η_B) decreases significantly with increasing Ni²⁺ substitution. The variation of saturation magnetization has been explained on the basis of Neel's molecular field theory. The coercive field(H_c) is found strongly dependent on the concentration of Ni²⁺ and decrease of coercivity suggests that the particles have single domain and exhibits superparamagnetic behavior. The Mössbauer spectroscopy shows two ferrimagnetically relaxed Zeeman sextets distribution at room temperature. The dependence of Mössbauer parameters such as isomer shift, quadru pole splitting, line width and hyperfine magnetic field on Ni²⁺ concentration have been discussed. Hence our results suggest that synthesized materials are potential candidate for power transformer application.     

Transport phenomena in laser surface alloying

A three dimensional, transient model is developed for studying heat transfer, fluid flow and mass transfer for the case of a single-pass laser surface alloying process. The numerical study is performed in a co-ordinate system fixed to the laser which moves with a constant scanning speed. The coupled momentum, energy and species conservation equations are solved using a finite volume technique. Phase change processes are modelled using a fixed-grid enthalpy-porosity technique, which is capable of predicting the continuously evolving solid-liquid interface. The three-dimensional model is able to predict the species concentration distribution inside the molten pool during alloying, as well as in the entire cross section of the solidified alloy. Corresponding experimental results show a good qualitative agreement with the numerical predictions with regard to pool shape and final composition distribution.     

Formation of a geometric pattern with a mobile wireless sensor network

Mobile wireless sensor networks (MWSNs) will enable information systems to gather detailed information about the environment on an unprecedented scale. These self‐organizing, distributed networks of sensors, processors, and actuators that are capable of movement have a broad range of potential applications, including military reconnaissance, surveillance, planetary exploration, and geophysical mapping. In many of the foreseen applications, the MWSN will need to form a geometric pattern without assistance from the user. In military reconnaissance, for example, the nodes will be dropped onto the battlefield from a plane and land at random positions. The nodes will be expected to arrange themselves into a predetermined formation in order to perform a specific task. Thus, we present algorithms for forming a line, circle, and regular polygon from a given set of random positions. The algorithms are distributed and use no communication between the nodes to minimize energy consumption. Unlike past studies of geometric problems where algorithms are either tested in simulations where each node has global knowledge of all the other nodes or implemented on a small number of robots, the robustness of our algorithms has been studied with simulations that model the sensor system in detail. The simulations demonstrate that the algorithms are robust against random errors in the sensors and actuators. © 2004 Wiley Periodicals, Inc.     

Bounds on the number of samples needed for neural learning.

The relationship between the number of hidden nodes in a neural network, the complexity of a multiclass discrimination problem, and the number of samples needed for effect learning are discussed. Bounds for the number of samples needed for effect learning are given. It is shown that Omega(min (d,n) M) boundary samples are required for successful classification of M clusters of samples using a two-hidden-layer neural network with d-dimensional inputs and n nodes in the first hidden layer.