Electrical and magneto-resistance of Co/CNT/epoxy thin film for strain and magnetic field sensing

Cobalt and iron nanoparticles are doped in carbon nanotube (CNT)/polymer matrix composites and studied for strain and magnetic field sensing properties. Characterization of these samples is done for various volume fractions of each constituent (Co and Fe nanoparticles and CNTs) and also for cases when only either of the metallic components is present. The relation between the magnetic field and polarization-induced strain are exploited. The electronic bandgap change in the CNTs is obtained by a simplified tight-binding formulation in terms of strain and magnetic field. A nonlinear constitutive model of glassy polymer is employed to account for (1) electric bias field dependent softening/hardening (2) CNT orientations as a statistical ensemble and (3) CNT volume fraction. An effective medium theory is then employed where the CNTs and nanoparticles are treated as inclusions. The intensity of the applied magnetic field is read indirectly as the change in resistance of the sample. Very small magnetic fields can be detected using this technique since the resistance is highly sensitive to strain. Its sensitivity due to the CNT volume fraction is also discussed. The advantage of this sensor lies in the fact that it can be molded into desirable shape and can be used in fabrication of embedded sensors where the material can detect external magnetic fields on its own. Besides, the stress-controlled hysteresis of the sample can be used in designing memory devices. These composites have potential for use in magnetic encoders, which are made of a magnetic field sensor and a barcode.

Electrical resistivity studies of benzidine-TCNQ and its inclusion compound under high pressure

Electrical resistivity studies of the charge transfer complex benzidine—TCNQ and its inclusion compound, have been carried out up to pressures 8 GPa. Two types of behaviour were observed in these complexes under high pressure and this difference is interpreted and discussed.

WEPA: Wind energy potential assessment-spatial decision support system

Spatial Decision Support System (SDSS) assist in strategic decision-making activities considering spatial and temporal variables, which help in Regional planning. WEPA is a SDSS designed for assessment of wind potential spatially. A wind energy system transforms the kinetic energy of the wind into mechanical or electrical energy that can be harnessed for practical use. Wind energy can diversify the economies of rural communities, adding to the tax base and providing new types of income. Wind turbines can add a new source of property value in rural areas that have a hard time attracting new industry. Wind speed is extremely important parameter for assessing the amount of energy a wind turbine can convert to electricity: The energy content of the wind varies with the cube (the third power) of the average wind speed. Estimation of the wind power potential for a site is the most important requirement for selecting a site for the installation of a wind electric generator and evaluating projects in economic terms. It is based on data of the wind frequency distribution at the site, which are collected from a meteorological mast consisting of wind anemometer and a wind vane and spatial parameters (like area available for setting up wind farm, landscape, etc.). The wind resource is governed by the climatology of the region concerned and has large variability with reference to space (spatial expanse) and time (season) at any fixed location. Hence the need to conduct wind resource surveys and spatial analysis constitute vital components in programs for exploiting wind energy. SDSS for assessing wind potential of a region / location is designed with user friendly GUI’s (Graphic User Interface) using VB as front end with MS Access database (backend). Validation and pilot testing of WEPA SDSS has been done with the data collected for 45 locations in Karnataka based on primary data at selected locations and data collected from the meteorological observatories of the India Meteorological Department (IMD). Wind energy and its characteristics have been analysed for these locations to generate user-friendly reports and spatial maps. Energy Pattern Factor (EPF) and Power Densities are computed for sites with hourly wind data. With the knowledge of EPF and mean wind speed, mean power density is computed for the locations with only monthly data. Wind energy conversion systems would be most effective in these locations during May to August. The analyses show that coastal and dry arid zones in Karnataka have good wind potential, which if exploited would help local industries, coconut and areca plantations, and agriculture. Pre-monsoon availability of wind energy would help in irrigating these orchards, making wind energy a desirable alternative.

Thermally induced phase separation in P alpha MSAN/PMMA blends in presence of functionalized multiwall carbon nanotubes: Rheology, morphology and electrical conductivity

The focus of this work is the evaluation and analysis of the state of dispersion of functionalized multiwall carbon nanotubes (CNTs), within different morphologies formed, in a model LCST blend (poly[(alpha-methylstyrene)-co-(acrylonitrile)]/poly(methyl-methacryla te), P alpha MSAN/PMMA). Blend compositions that are expected to yield droplet-matrix (85/15 P alpha MSAN/PMMA and 15/85 P alpha MSAN/PMMA, wt/wt) and co-continuous morphologies (60/40 P alpha MSAN/PMMA, wt/wt) upon phase separation have been combined with two types of CNTs; carboxylic acid functionalized (CNTCOOH) and polyethylene modified (CNTPE) up to 2 wt%. Thermally induced phase separation in the blends has been studied in-situ by rheology and dielectric (conductivity) spectroscopy in terms of morphological evolution and CNT percolation. The state of dispersion of CNTs has been evaluated by transmission electron microscopy. The experimental results indicate that the final blend morphology and the surface functionalization of CNT are the main factors that govern percolation. In presence of either of the CNTs, 60/40 P alpha MSAN/PMMA blends yield a droplet-matrix morphology rather than co-continuous and do not show any percolation. On the other hand, both 85/15 P alpha MSAN/PMMA and 15/85 P alpha MSAN/PMMA blends containing CNTPEs show percolation in the rheological and electrical properties. Interestingly, the conductivity spectroscopy measurements demonstrate that the 15/85 P alpha MSAN/PMMA blends with CNTPEs that show insulating properties at room temperature for the miscible blends reveal highly conducting properties in the phase separated blends (melt state) as a result of phase separation. By quenching this morphology, the conductivity can be retained in the blends even in the solid state. (C) 2011 Elsevier Ltd. All rights reserved.

Wireless Sensor Networks for Organizational Network Analysis

Since their emergence, wireless sensor networks (WSNs) have become increasingly popular in the pervasive computing industry. This is particularly true within the past five years, which has seen sensor networks being adapted for wide variety of applications. Most of these applications are restricted to ambience monitoring and military use, however, very few commercial sensor applications have been explored till date. For WSNs to be truly ubiquitous, many more commercial sensor applications are yet to be investigated. As an effort to probe for such an application, we explore the potential of using WSNs in the field of Organizational Network Analysis (ONA). In this short paper, we propose a WSN based framework for analyzing organizational networks. We describe the role of WSNs in learning relationships among the people of an organization and investigate the research challenges involved in realizing the proposed framework.