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.

A comparative study of ordinary kriging and support vector machine models for the spatial variability of rock depth in Bangalore

In this paper, reduced level of rock at Bangalore, India is arrived from the 652 boreholes data in the area covering 220 sq.km. In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth, ordinary kriging and Support Vector Machine (SVM) models have been developed. In ordinary kriging, the knowledge of the semivariogram of the reduced level of rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of Bangalore, where field measurements are not available. A cross validation (Q1 and Q2) analysis is also done for the developed ordinary kriging model. The SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing e-insensitive loss function has been used to predict the reduced level of rock from a large set of data. A comparison between ordinary kriging and SVM model demonstrates that the SVM is superior to ordinary kriging in predicting rock depth.

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.

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.

Multiclass Core Vector Machine

Even though several techniques have been proposed in the literature for achieving multiclass classification using Support Vector Machine(SVM), the scalability aspect of these approaches to handle large data sets still needs much of exploration. Core Vector Machine(CVM) is a technique for scaling up a two class SVM to handle large data sets. In this paper we propose a Multiclass Core Vector Machine(MCVM). Here we formulate the multiclass SVM problem as a Quadratic Programming(QP) problem defining an SVM with vector valued output. This QP problem is then solved using the CVM technique to achieve scalability to handle large data sets. Experiments done with several large synthetic and real world data sets show that the proposed MCVM technique gives good generalization performance as that of SVM at a much lesser computational expense. Further, it is observed that MCVM scales well with the size of the data set.