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.     

Estimation of power density in IMPATT using different materials

ABSTRACT

The RF output power dissipated per unit area is calculated using Runge-Kutta method for the high-moderate-moderate-high (n⁺-n-p-p⁺) doping profile of double drift region (DDR)-based impact avalanche transit time (IMPATT) diode by taking different substrate at Ka band. Those substrates are silicon, gallium arsenide, germanium, wurtzite gallium nitride, indium phosphide and 4H-silicon carbide. A comparative study regarding power dissipation ability by the IMPATT using different material is being presented thereby modelling the DDR IMPATT diode in a one-dimensional structure. The IMPATT based on 4H-SiC element has highest power density in the order of 10¹⁰ Wm^?2 and the Si-based counterpart has lowest power density of order 10⁶ Wm^?2 throughout the Ka band. So, 4H-SiC-based IMPATT should be preferable over others for the power density preference based application. This result will be helpful to estimate the power density of the IMPATT for any doping profile and to select the proper element for the optimum design of the IMPATT as far as power density is concerned in the Ka band. Also, we have focused on variation of power density with different junction temperatures and modelled the heat sink with analysis of thermal resistances.     

Beneficial effects of wettability altering surfactants in oil-wet fractured reservoirs

In fractured reservoirs, an effective matrix-fracture mass transfer is required for oil recovery. Surfactants have long been considered for oil recovery enhancement, mainly in terms of their ability to reduce oil–water interfacial tension. These surfactants are effective when the fractured formations are water-wet, where capillary imbibition of surfactants from the fracture into the matrix contributes to oil recovery. However, another beneficial aspect of surfactants, namely their ability to alter wettability, remains to be explored and exploited. Surfactants capable of altering wettability can be especially beneficial in oil-wet fractured formations, where the surfactant in the fracture diffuses into the matrix and alters the wettability, enabling imbibition of even more surfactant into the matrix. This sequential process of initial diffusion followed by imbibition continues well into the matrix yielding significant enhancements in oil recovery.In order to test this hypothesis of sequential diffusion–imbibition phenomenon, Dual-Drop Dual-Crystal (DDDC) contact angle experiments have been conducted using fractured Yates dolomite reservoir fluids, two types of surfactants (nonionic and anionic) and dolomite rock substrates. A new experimental procedure was developed in which crude oil equilibrated with reservoir brine has been exposed to surfactant to simulate the matrix-fracture interactions in fractured reservoirs. This procedure enables the measurements of dynamic contact angles and oil–water interfacial tensions, in addition to providing the visual observations of the dynamic behavior of crude oil trapped in the rock matrix as it encounters the diffusing surfactant from the fractures. Both the measurements and visual observations indicate wettability alterations of the matrix surface from oil-wet to less oil-wet or intermediate wet by the surfactants. Thus this study is of practical importance to oil-wet fractured formations where surfactant-induced wettability alterations can result in significant oil recovery enhancements. In addition, this study has also identified the need to include contact angle term in the dimensionless Bond number formulations for better quantitative interpretation of rock–fluids interactions.     

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.     

Biological and health implications of toxic heavy metal and essential trace element interactions

Human civilization and a concomitant increase in industrial activity has gradually redistributed many toxic metals from the earth's crust to the environment and increased the possibility of human exposure. Among the various toxic elements, heavy metals cadmium, lead, and mercury are specially prevalent in nature due to their high industrial use. These metals serve no biological function and their presence in tissues reflects contact of the organism with its environment. They are cumulative poison, and are toxic even at low dose. Studies of metabolism and toxicity of these elements have revealed important interactions between them and some essential dietary elements like calcium, zinc, iron, selenium, copper, chromium, and manganese. In general, a deficiency of these essential elements increases toxicity of heavy metals, whereas an excess appears to be protective. While most of the observations are on laboratory animals, limited human data are in agreement with the results of animal experiments. These suggest that the dietary presence of the essential elements may contribute to the protection of man and animal from the effects of heavy metal exposure, while their deficiency may increase toxicity. Appropriate dietary manipulation thus may be valuable in the prevention and treatment of heavy metal toxicity.