Microstructure Development, Nanomechanical, and Dynamic Compression Properties of Spark Plasma Sintered TiB2-Ti-Based Homogeneous and Bi-layered Composites

The growing threats due to increased use of small-caliber armor piercing projectiles demand the development of new light-weight body armor materials. In this context, TiB2 appears to be a promising ceramic material. However, poor sinterability and low fracture toughness remain two major issues for TiB2. In order to address these issues together, Ti as a sinter-aid is used to develop TiB2-(x wt pct Ti), (x = 10, 20) homogeneous composites and a bi-layered composite (BLC) with each layer having Ti content of 10 and 20 wt pct. The present study uniquely demonstrates the efficacy of two-stage spark plasma sintering route to develop dense TiB2-Ti composites with an excellent combination of nanoscale hardness (similar to 36 GPa) and indentation fracture toughness (similar to 12 MPa m(1/2)). In case of BLC, these properties are not compromised w.r.t. homogeneous composites, suggesting the retention of baseline material properties even in the bi-layer design due to optimal relief of residual stresses. The better indentation toughness of TiB2-(10 wt pct Ti) and TiB2-(20 wt pct Ti) composites can be attributed to the observed crack deflection/arrest, indicating better damage tolerance. Transmission electron microscope investigation reveals the presence of dense dislocation networks and deformation twins in alpha-Ti at the grain boundaries and triple pockets, surrounded by TiB2 grains. The dynamic strength of around 4 GPa has been measured using Split Hopkinson Pressure Bar tests in a reproducible manner at strain rates of the order of 600 s(-1). The damage progression under high strain rate has been investigated by acquiring real time images for the entire test duration using ultra-high speed imaging. An attempt has been made to establish microstructure-property correlation and a simple analysis based on Mohr-Coulomb theory is used to rationalize the measured strength properties.

Effect of functional groups on separating carbon dioxide from CO2/N-2 gas mixtures using edge functionalized graphene nanoribbons

Development of microporous adsorbents for separation and sequestration of carbon dioxide from flue gas streams is an area of active research. In this study, we assess the influence of specific functional groups on the adsorption selectivity of CO2/N-2 mixtures through Grand Canonical Monte Carlo (GCMC) simulations. Our model system consists of a bilayer graphene nanoribbon that has been edge functionalized with OH, NH2, NO2, CH3 and COOH. Ab initio Moller-Plesset (MP2) calculations with functionalized benzenes are used to obtain binding energies and optimized geometries for CO2 and N-2. This information is used to validate the choice classical forcefields in GCMC simulations. In addition to simulations of adsorption from binary mixtures of CO2 and N-2, the ideal adsorbed solution theory (IAST) is used to predict mixture isotherms. Our study reveals that functionalization always leads to an increase in the adsorption of both CO2 and N-2 with the highest for COOH. However, significant enhancement in the selectivity for CO2 is only seen with COOH functionalized nanoribbons. The COOH functionalization gives a 28% increase in selectivity compared to H terminated nanoribbons, whereas the improvement in the selectivity for other functional groups are much Enure modest. Our study suggests that specific functionalization with COOH groups can provide a material's design strategy to improve CO2 selectivity in microporous adsorbents. Synthesis of graphene nanoplatelets with edge functionalized COOH, which has the potential for large scale production, has recently been reported (Jeon el, al., 2012). (C) 2014 Elsevier Ltd. All rights reserved,

Modification of crosslinked chitosan membrane using NaY zeolite for pervaporation separation of water-isopropanol mixtures

The blocked diisocyanate crosslinked chitosan membrane was modified by incorporating different mass% of NaY zeolite. The physico-chemical properties of resulting composite membranes were studied using Fourier transform infrared spectroscopy (FTIR), wide-angle X-ray diffraction (WAXD), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC) and scanning electron microscopy (SEM). The mechanical properties of the membranes were studied using universal testing machine (UTM). After measuring the equilibrium swelling, membranes were subjected to pervaporation for separation of water-isopropanol mixtures. Both flux and selectivity were increased with increasing NaY zeolite content in the membranes. The membrane containing 40 mass% of NaY zeolite exhibited the highest separation selectivity of 11,241 with a flux of 11.37 x 10(-2) kg/m(2) h for 10 mass% of water in the feed. The total flux and flux of water are almost overlapping each other, suggesting that these membranes could be effectively used to break the azeotropic point of water-isopropanol mixture. From the temperature dependent diffusion and permeation values, the Arrhenius activation parameters were estimated. All the composite membranes exhibited lower activation energy compared to crosslinked membrane, indicating that the permeants require less energy during the process because of molecular sieving action attributed to the presence of sodalite and super cages in the framework of Nay zeolite. The Henry's mode of sorption dominates the process, giving an endothermic contribution. (C) 2014 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

Graphene oxide-Fe3O4 nanoparticle composite with high transverse proton relaxivity value for magnetic resonance imaging

The potential of graphene oxide-Fe3O4 nanoparticle (GO-Fe3O4) composite as an image contrast enhancing material in magnetic resonance imaging has been investigated. Proton relaxivity values were obtained in three different homogeneous dispersions of GO-Fe3O4 composites synthesized by precipitating Fe3O4 nanoparticles in three different reaction mixtures containing 0.01 g, 0.1 g, and 0.2 g of graphene oxide. A noticeable difference in proton relaxivity values was observed between the three cases. A comprehensive structural and magnetic characterization revealed discrete differences in the extent of reduction of the graphene oxide and spacing between the graphene oxide sheets in the three composites. The GO-Fe3O4 composite framework that contained graphene oxide with least extent of reduction of the carboxyl groups and largest spacing between the graphene oxide sheets provided the optimum structure for yielding a very high transverse proton relaxivity value. It was found that the GO-Fe3O4 composites possessed good biocompatibility with normal cell lines, whereas they exhibited considerable toxicity towards breast cancer cells. (C) 2015 AIP Publishing LLC.

Two-dimensional homogeneous isotropic fluid turbulence with polymer additives

We carry out an extensive and high-resolution direct numerical simulation of homogeneous, isotropic turbulence in two-dimensional fluid films with air-drag-induced friction and with polymer additives. Our study reveals that the polymers (a) reduce the total fluid energy, enstrophy, and palinstrophy; (b) modify the fluid energy spectrum in both inverse-and forward-cascade regimes; (c) reduce small-scale intermittency; (d) suppress regions of high vorticity and strain rate; and (e) stretch in strain-dominated regions. We compare our results with earlier experimental studies and propose new experiments.

Homogeneous isotropic superfluid turbulence in two dimensions: Inverse and forward cascades in the Hall-Vinen-Bekharevich-Khalatnikov model

We present the first direct-numerical-simulation study of the statistical properties of two-dimensional superfluid turbulence in the simplified, Hall-Vinen-Bekharevich-Khalatnikov two-fluid model. We show that both normalfluid and superfluid energy spectra can exhibit two power-law regimes, the first associated with an inverse cascade of energy and the second with the forward cascade of enstrophy. We quantify the mutual-friction-induced alignment of normal and superfluid velocities by obtaining probability distribution functions of the angle between them and the ratio of their moduli.

ELEVATION OF HEAT REJECTION TEMPERATURE IN TRANSCRITICAL CONDENSING CYCLES USING CO2+PROPANE MIXTURES

In this paper, a detailed thermodynamic performance analysis of a transcritical condensing (TC) cycle is performed with pure CO2 and a blend of 48.5 % propane with 51.5 % CO2 as working fluids. A realistic thermodynamic model is used incorporating irreversibilities in turbo-machineries and heat exchangers. The Key finding is that the addition of propane elevates the heat rejection temperature, but does not impair any of the performance indicators. Such a fluid may be useful for power generation in concentrated solar power applications by using which a hike of up to 2 % can be realized in the thermal efficiency of a power plant.

An Exponential Lower Bound for Homogeneous Depth Four Arithmetic Formulas

We show here a 2(Omega(root d.log N)) size lower bound for homogeneous depth four arithmetic formulas. That is, we give an explicit family of polynomials of degree d on N variables (with N = d(3) in our case) with 0, 1-coefficients such that for any representation of a polynomial f in this family of the form f = Sigma(i) Pi(j) Q(ij), where the Q(ij)'s are homogeneous polynomials (recall that a polynomial is said to be homogeneous if all its monomials have the same degree), it must hold that Sigma(i,j) (Number of monomials of Q(ij)) >= 2(Omega(root d.log N)). The above mentioned family, which we refer to as the Nisan-Wigderson design-based family of polynomials, is in the complexity class VNP. Our work builds on the recent lower bound results 1], 2], 3], 4], 5] and yields an improved quantitative bound as compared to the quasi-polynomial lower bound of 6] and the N-Omega(log log (N)) lower bound in the independent work of 7].

Delineation of homogeneous temperature regions: a two-stage clustering approach

Homogeneous temperature regions are necessary for use in hydrometeorological studies. The regions are often delineated by analysing statistics derived from time series of maximum, minimum or mean temperature, rather than attributes influencing temperature. This practice cannot yield meaningful regions in data-sparse areas. Further, independent validation of the delineated regions for homogeneity in temperature is not possible, as temperature records form the basis to arrive at the regions. To address these issues, a two-stage clustering approach is proposed in this study to delineate homogeneous temperature regions. First stage of the approach involves (1) determining correlation structure between observed temperature over the study area and possible predictors (large-scale atmospheric variables) influencing the temperature and (2) using the correlation structure as the basis to delineate sites in the study area into clusters. Second stage of the approach involves analysis on each of the clusters to (1) identify potential predictors (large-scale atmospheric variables) influencing temperature at sites in the cluster and (2) partition the cluster into homogeneous fuzzy temperature regions using the identified potential predictors. Application of the proposed approach to India yielded 28 homogeneous regions that were demonstrated to be effective when compared to an alternate set of 6 regions that were previously delineated over the study area. Intersite cross-correlations of monthly maximum and minimum temperatures in the existing regions were found to be weak and negative for several months, which is undesirable. This problem was not found in the case of regions delineated using the proposed approach. Utility of the proposed regions in arriving at estimates of potential evapotranspiration for ungauged locations in the study area is demonstrated.

Delineation of homogeneous hydrometeorological regions using wavelet-based global fuzzy cluster analysis

Identification of homogeneous hydrometeorological regions (HMRs) is necessary for various applications. Such regions are delineated by various approaches considering rainfall and temperature as two key variables. In conventional approaches, formation of regions is based on principal components (PCs)/statistics/indices determined from time series of the key variables at monthly and seasonal scales. An issue with use of PCs for regionalization is that they have to be extracted from contemporaneous records of hydrometeorological variables. Therefore, delineated regions may not be effective when the available records are limited over contemporaneous time period. A drawback associated with the use of statistics/indices is that they do not provide effective representation of the key variables when the records exhibit non-stationarity. Consequently, the resulting regions may not be effective for the desired purpose. To address these issues, a new approach is proposed in this article. The approach considers information extracted from wavelet transformations of the observed multivariate hydrometeorological time series as the basis for regionalization by global fuzzy c-means clustering procedure. The approach can account for dynamic variability in the time series and its non-stationarity (if any). Effectiveness of the proposed approach in forming HMRs is demonstrated by application to India, as there are no prior attempts to form such regions over the country. Drought severity-area-frequency (SAF) curves are constructed corresponding to each of the newly formed regions for the use in regional drought analysis, by considering standardized precipitation evapotranspiration index (SPEI) as the drought indicator.

Complex patterns arise through spontaneous symmetry breaking in dense homogeneous networks of neural oscillators

There has been much interest in understanding collective dynamics in networks of brain regions due to their role in behavior and cognitive function. Here we show that a simple, homogeneous system of densely connected oscillators, representing the aggregate activity of local brain regions, can exhibit a rich variety of dynamical patterns emerging via spontaneous breaking of permutation or translational symmetries. Upon removing just a few connections, we observe a striking departure from the mean-field limit in terms of the collective dynamics, which implies that the sparsity of these networks may have very important consequences. Our results suggest that the origins of some of the complicated activity patterns seen in the brain may be understood even with simple connection topologies.

Homogeneous Hermitian holomorphic vector bundles and the Cowen-Douglas class over bounded symmetric domains

It is known that all the vector bundles of the title can be obtained by holomorphic induction from representations of a certain parabolic group on finite-dimensional inner product spaces. The representations, and the induced bundles, have composition series with irreducible factors. We write down an equivariant constant coefficient differential operator that intertwines the bundle with the direct sum of its irreducible factors. As an application, we show that in the case of the closed unit ball in C-n all homogeneous n-tuples of Cowen-Douglas operators are similar to direct sums of certain basic n-tuples. (c) 2015 Academie des sciences. Published by Elsevier Masson SAS. All rights reserved.