Terahertz Spectroscopy of Single-Walled Carbon Nanotubes in a Polymer Film: Observation of Low-Frequency Phonons

We investigate the dielectric response of single-walled carbon nanotubes dispersed in poly(vinyl alcohol) matrix by using terahertz time domain spectroscopy. Frequency-dependent real and imaginary parts of the complex dielectric function are measured experimentally in the terahertz regime. The low-frequency phonons of carbon nanotubes, though predicted theoretically, are directly observed for the first time at frequencies 0.26, 0.60, and 0.85 THz. Further, a broad resonance is observed at 1.15 THz associated with the longitudinal acoustic mode of vibration of straight-chain segments of the long polymeric molecules in the film. The latter is observed at 1.24 THz for a pristine polymer film and has been used to derive the size of crystalline lamellae in the film.

Environmental effects on fibre-Polymer composites

Moisture absorption characteristics and its effects on the mechanical properties and failure process of polymers (neat epoxy and polyester resins) and composites with simple (glass, carbon and kevlar) and hybrid (glass-carbon, carbon-kevlar and kevlar-glass) fibres were experimentally determined before and after immersion in water at 343 K for 20 days. The maximum moisture content (Mm) and diffusion coefficient (Dx) of these composites were determined. The degradation in ultimate tensile strength and Young's modulus due to the moisture content were experimentally determined and found to be quite significant. Acoustic emissions, from specimens before and after exposure, were monitored during the load cycle, and revealed a significant change in the failure process of these composites. Scanning Electron Microscope (SEM) studies on failed exposed and unexposed specimens revealed resin leach out and fibre prominence.

Modeling server-unreliability in closed queuing-networks

A method is presented to model server unreliability in closed queuing networks. Breakdowns and repairs of servers, assumed to be time-dependent, are modeled using virtual customers and virtual servers in the system. The problem is thus converted into a closed queue with all reliable servers and preemptive resume priority centers. Several recent preemptive priority approximations and an approximation of the one proposed are used in the analysis. This method has approximately the same computational requirements as that of mean-value analysis for a network of identical dimensions and is therefore very efficient

Scaling behavior of plasmon coupling in Au and ReO3 nanoparticles incorporated in polymer matrices

Polymer nanocomposites containing different concentrations of Au nanoparticles have been investigated by small angle X-ray scattering and electronic absorption spectroscopy. The variation in the surface plasmon resonance (SPR) band of Au nanoparticles with concentration is described by a scaling law. The variation in the plasmon band of ReO3 nanoparticles embedded in polymers also follows a similar scaling law. Sistance dependence of plasmon coupling in polymer composites f metal nanoparticles. (C) 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Low-temperature polymer precursor-based synthesis of nanocrystalline particles of lanthanum calcium manganese oxide (La0.67Ca0.33MnO3) with enhanced ferromagnetic transition temperature

We report a simple modified polymeric precursor route for the synthesis of highly crystalline and homogenous nanoparticles of lanthanum calcium manganese oxide (LCMO). The LCMO phase formation was studied by thermal analysis, x-ray powder diffraction, and infrared spectroscopy at different stages of heating. These nanocrystallites (average particle size of 30 nm) possess ferromagnetic-paramagnetic transition temperature (T-c) of 300 K, nearly 50 K higher than that of a single crystal. The Rietveld analysis of the powder x-ray diffraction data of the nanopowders reveals significant lattice contraction and reduction in unit cell anisotropy-these structural changes are correlated to the enhancement in T-c.

Studies on a nanocomposite solid polymer electrolyte with hydrotalcite as a filler

We have prepared a new nanocomposite polymer electrolyte using nanoparticles of hydrotalcite, an anionic clay, as the filler. Hydrotalcite has the chemical composition [M-1-x(2+) M-x(3+) (OH)(2)](x+) [A(x/n)(n-)center dot mH(2)O] where M2+ is a divalent cation (e.g. Mg2+, Ni2+, Co2+,etc.) and M3+ is a trivalent cation (e.g. Al3+, Fe3+, Cr3+, etc.). A(n-) is an anion intercalated between the positively charged double hydroxide layers. The nanoparticles of [Mg0.67Al0.33 (OH)(2)] [(CO3)(0.17)center dot mH(2)O] were prepared by the co-precipitation method (average particle size as observed by TEM similar to 50 nm) and were doped into poly(ethylene glycol) PEG (m.w.2000) complexed with LiCIO4. Samples with different wt.% of hydrotalcite were prepared and characterized using XRD, DSC, TGA, impedance spectroscopy and NMR. Ionic conductivity for the pristine sample, similar to 7.3 x 10(-7) S cm(-1), was enhanced to a maximum of = 1.1 x 10(-5) S cm(-1) for 3.6 wt.% nanoparticle doped sample. We propose that the enhancement of ionic conductivity is caused by percolation effects of the high conductivity paths provided by interfaces between the nanoparticles and the polymer electrolyte. (C) 2010 Elsevier B.V. All rights reserved.

Communication: Evidence of dynamic heterogeneity in glassy polymer monolayers from interface microrheology measurements

We have developed a novel nanoparticle tracking based interface microrheology technique to perform in situ studies on confined complex fluids. To demonstrate the power of this technique, we show, for the first time, how in situ glass formation in polymers confined at air-water interface can be directly probed by monitoring variation of the mean square displacement of embedded nanoparticles as a function of surface density. We have further quantified the appearance of dynamic heterogeneity and hence vitrification in polymethyl methacrylate monolayers above a certain surface density, through the variation of non-Gaussian parameter of the probes. (C) 2010 American Institute of Physics. [doi:10.1063/1.3471584].

Medium access control schemes for local area networks with multiple priority function

This paper deals with the development and performance evaluation of three modified versions of a scheme proposed for medium access control in local area networks. The original scheme implements a collision-free and fair medium arbitration by using a control wire in conjunction with a data bus. The modifications suggested in this paper are intended to realize the multiple priority function in local area networks.

Joint Power Control, Scheduling and Routing for Multihop Energy Harvesting Sensor Networks

We study wireless multihop energy harvesting sensor networks employed for random field estimation. The sensors sense the random field and generate data that is to be sent to a fusion node for estimation. Each sensor has an energy harvesting source and can operate in two modes: Wake and Sleep. We consider the problem of obtaining jointly optimal power control, routing and scheduling policies that ensure a fair utilization of network resources. This problem has a high computational complexity. Therefore, we develop a computationally efficient suboptimal approach to obtain good solutions to this problem. We study the optimal solution and performance of the suboptimal approach through some numerical examples.

Artificial neural networks and multicriterion analysis for sustainable irrigation planning

The objective of the present paper is to select the best compromise irrigation planning strategy for the case study of Jayakwadi irrigation project, Maharashtra, India. Four-phase methodology is employed. In phase 1, separate linear programming (LP) models are formulated for the three objectives, namely. net economic benefits, agricultural production and labour employment. In phase 2, nondominated (compromise) irrigation planning strategies are generated using the constraint method of multiobjective optimisation. In phase 3, Kohonen neural networks (KNN) based classification algorithm is employed to sort nondominated irrigation planning strategies into smaller groups. In phase 4, multicriterion analysis (MCA) technique, namely, Compromise Programming is applied to rank strategies obtained from phase 3. It is concluded that the above integrated methodology is effective for modeling multiobjective irrigation planning problems and the present approach can be extended to situations where number of irrigation planning strategies are even large in number. (c) 2004 Elsevier Ltd. All rights reserved.

Karnaugh map synthesis of multigate threshold networks

It has been shown in an earlier paper that I-realizability of a unate function F of up to six variables corresponds to ' compactness ' of the plot of F on a Karnaugh map. Here, an algorithm has been presented to synthesize on a Karnaugh map a non-threahold function of up to Bix variables with the minimum number of threshold gates connected in cascade. Incompletely specified functions can also be treated. No resort to inequalities is made and no pre-processing (such as positivizing and ordering) of the given switching function is required.

Regenerating Codes for Distributed Storage Networks

In a storage system where individual storage nodes are prone to failure, the redundant storage of data in a distributed manner across multiple nodes is a must to ensure reliability. Reed-Solomon codes possess the reconstruction property under which the stored data can be recovered by connecting to any k of the n nodes in the network across which data is dispersed. This property can be shown to lead to vastly improved network reliability over simple replication schemes. Also of interest in such storage systems is the minimization of the repair bandwidth, i.e., the amount of data needed to be downloaded from the network in order to repair a single failed node. Reed-Solomon codes perform poorly here as they require the entire data to be downloaded. Regenerating codes are a new class of codes which minimize the repair bandwidth while retaining the reconstruction property. This paper provides an overview of regenerating codes including a discussion on the explicit construction of optimum codes.

Voltage and Temperature Aware Statistical Leakage Analysis Framework Using Artificial Neural Networks

Artificial neural networks (ANNs) have shown great promise in modeling circuit parameters for computer aided design applications. Leakage currents, which depend on process parameters, supply voltage and temperature can be modeled accurately with ANNs. However, the complex nature of the ANN model, with the standard sigmoidal activation functions, does not allow analytical expressions for its mean and variance. We propose the use of a new activation function that allows us to derive an analytical expression for the mean and a semi-analytical expression for the variance of the ANN-based leakage model. To the best of our knowledge this is the first result in this direction. Our neural network model also includes the voltage and temperature as input parameters, thereby enabling voltage and temperature aware statistical leakage analysis (SLA). All existing SLA frameworks are closely tied to the exponential polynomial leakage model and hence fail to work with sophisticated ANN models. In this paper, we also set up an SLA framework that can efficiently work with these ANN models. Results show that the cumulative distribution function of leakage current of ISCAS'85 circuits can be predicted accurately with the error in mean and standard deviation, compared to Monte Carlo-based simulations, being less than 1% and 2% respectively across a range of voltage and temperature values.