375 resultados para strain-reduction


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In last 40 years, catalysis for NO (x) removal from exhaust gas has received much attention to achieve pollution free environment. CeO(2) has been found to play a major role in the area of exhaust catalysis due to its unique redox properties. In last several years, we have been exploring an entirely new approach of dispersing noble metal ions in CeO(2) and TiO(2) for redox catalysis. We have extensively studied Ce(1-x) M (x) O(2-delta) (M = Pd, Pt, Rh), Ce(1-x-y) A (x) M (y) O(2-delta) (A = Ti, Zr, Sn, Fe; M = Pd, Pt) and Ti(1-x) M (x) O(2-delta) (M = Pd, Pt, Rh, Ru) catalysts for exhaust catalysis especially NO reduction and CO oxidation, structure-property relation and mechanism of catalytic reactions. In these catalysts, lower valent noble metal ion substitution in CeO(2) and TiO(2) creates noble metal ionic sites and oxide ion vacancy. NO gets molecularly adsorbed on noble metal ion site and dissociatively adsorbed on oxide ion vacancy site. Dissociative chemisorption of NO on oxide ion vacancy leads to preferential conversion of NO to N(2) instead of N(2)O over these catalysts. It has been demonstrated that these new generation noble metal ionic catalysts (NMIC) are much more catalytically active than conventional nano crystalline noble metal catalysts especially for NO reduction.

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The removal of native oxide from Si (1 1 1) surfaces was investigated by X-ray photoelectron spectroscopy (XPS) and secondary ion mass spectra (SIMS) depth profiles. Two different oxide removal methods, performed under ultrahigh-vacuum (UHV) conditions, were carried out and compared. The first cleaning method is thermal desorption of oxide at 900 degrees C. The second method is the deposition of metallic gallium followed by redesorption. A significant decrease in oxygen was achieved by thermal desorption at 900 degrees C under UHV conditions. By applying a subsequent Ga deposition/redesorption, a further reduction in oxygen could be achieved. We examine the merits of an alternative oxide desorption method via conversion of the stable SiO(2) surface oxide into a volatile Ca(2)O oxide by a supply of Ga metals. Furthermore, ultra thin films of pure silicon nitride buffer layer were grown on a Si (1 1 1) surface by exposing the surface to radio-frequency (RF) nitrogen plasma followed by GaN growth. The SIMS depth profile shows that the oxygen impurity can be reduced at GaN/beta-Si(3)N(4)/Si interfaces by applying a subsequent Ga deposition/redesorption. (C) 2011 Elsevier B.V. All rights reserved.

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In this paper, the effects of T -stress on steady, dynamic crack growth in an elastic-plastic material are examined using a modified boundary layer formulation. The analyses are carried out under mode I, plane strain conditions by employing a special finite element procedure based on moving crack tip coordinates. The material is assumed to obey the J (2) flow theory of plasticity with isotropic power law hardening. The results show that the crack opening profile as well as the opening stress at a finite distance from the tip are strongly affected by the magnitude and sign of the T -stress at any given crack speed. Further, it is found that the fracture toughness predicted by the analyses enhances significantly with negative T -stress for both ductile and cleavage mode of crack growth.

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Fe-Cr/Al2O3 metal-ceramic composites prepared by hydrogen reduction at different temperatures and for different periods have been investigated by a combined use of Mössbauer spectroscopy, x-ray diffraction, transmission electron microscopy, and energy-dispersive x-ray spectroscopy in order to obtain information on the nature of the metallic species formed. Total reduction of Fe3+ does not occur by increasing the reduction time at 1320 K from 1 to 30 h, and the amount of superparamagnetic metallic species is essentially constant (about 10%). Temperatures higher than 1470 K are needed to achieve nearly total reduction of substitutional Fe3+. Interestingly, iron favors the reduction of chromium. The composition of the Fe-Cr particles is strongly dependent on their size, the Cr content being higher in particles smaller than 10 nm.

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The severe wear of a near eutectic aluminium silicon alloy is explored using a range of electron microscopic, spectroscopic and diffraction techniques to identify the residually strained and unstrained regions, microcracks and oxidized regions in the subsurface. In severe wear the contact pressure exceeds the elastic shakedown limit. Under this condition the primary and eutectic silicon particles fragment drastically. The fragments are transported by the matrix as it undergoes incremental straining with each cyclic contact at the asperity level. The grains are refined from similar to 2000 nm in the bulk to 30 nm in the near surface region. A large reduction in the interparticle distance compared with that for a milder stage of wear gives rise to high strain gradients which contribute to an enhancement of the dislocation density. The resulting regions of very high strain in the boundaries of the recrystallized grains as well as within the subgrains lead to the formation of microvoidskracks. This is accompanied by the formation of brittle oxides at these subsurface interfaces due to enhanced diffusion of oxygen. We believe that the abundance of such microcracks in the near surface region, primed by severe plastic deformation, is what distinguishes a severe wear regime from mild wear. (C) 2011 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

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Carbon-supported Pt-Au (Pt-Au/C) catalyst is prepared separately by impregnation, colloidal and micro-emulsion methods, and characterized by physical and electrochemical methods. Highest catalytic activity towards oxygen-reduction reaction (ORR) is exhibited by Pt-Au/C catalyst prepared by colloidal method. The optimum atomic ratio of Pt to Au in Pt-Au/C catalyst prepared by colloidal method is determined using linear-sweep and cyclic voltammetry in conjunction with cell-polarization studies. Among 3:1, 2:1 and 1:1 Pt-Au/C catalysts, (3:1) Pt-Au/C exhibits maximum electrochemical activity towards ORR. Powder X-ray diffraction pattern and transmission electron micrograph suggest Pt-Au alloy nanoparticles to be well dispersed onto the carbon-support. Energy dispersive X-ray analysis and inductively coupled plasma-optical emission spectroscopy data suggest that the atomic ratios of the alloying elements match well with the expected values. A polymer electrolyte fuel cell (PEFC) operating at 0 center dot 6 V with (3:1) Pt-Au/C cathode delivers a maximum power-density of 0 center dot 65 W/cm (2) in relation to 0 center dot 53 W/cm (2) delivered by the PEFC with pristine carbon-supported Pt cathode.

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Size and strain rate effects are among several factors which play an important role in determining the response of nanostructures, such as their deformations, to the mechanical loadings. The mechanical deformations in nanostructure systems at finite temperatures are intrinsically dynamic processes. Most of the recent works in this context have been focused on nanowires [1, 2], but very little attention has been paid to such low dimensional nanostructures as quantum dots (QDs). In this contribution, molecular dynamics (MD) simulations with an embedded atom potential method(EAM) are carried out to analyse the size and strain rate effects in the silicon (Si) QDs, as an example. We consider various geometries of QDs such as spherical, cylindrical and cubic. We choose Si QDs as an example due to their major applications in solar cells and biosensing. The analysis has also been focused on the variation in the deformation mechanisms with the size and strain rate for Si QD embedded in a matrix of SiO2 [3] (other cases include SiN and SiC matrices).It is observed that the mechanical properties are the functions of the QD size, shape and strain rate as it is in the case for nanowires [2]. We also present the comparative study resulted from the application of different EAM potentials in particular, the Stillinger-Weber (SW) potential, the Tersoff potentials and the environment-dependent interatomic potential (EDIP) [1]. Finally, based on the stabilized structural properties we compute electronic bandstructures of our nanostructures using an envelope function approach and its finite element implementation.

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This paper describes a dynamic voltage frequency control scheme for a 256 X 64 SRAM block for reducing the energy in active mode and stand-by mode. The DVFM control system monitors the external clock and changes the supply voltage and the body bias so as to achieve a significant reduction in energy. The behavioral model of the proposed DVFM control system algorithm is described and simulated in HDL using delay and energy parameters obtained through SPICE simulation. The frequency range dictated by an external controller is 100 MHz to I GHz. The supply voltage of the complete memory system is varied in steps of 50 mV over the range of 500 mV to IV. The threshold voltage range of operation is plusmn100 mV around the nominal value, achieving 83.4% energy reduction in the active mode and 86.7% in the stand-by mode. This paper also proposes a energy replica that is used in the energy monitor subsystem of the DVFM system.

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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.

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Estimates of predicate selectivities by database query optimizers often differ significantly from those actually encountered during query execution, leading to poor plan choices and inflated response times. In this paper, we investigate mitigating this problem by replacing selectivity error-sensitive plan choices with alternative plans that provide robust performance. Our approach is based on the recent observation that even the complex and dense "plan diagrams" associated with industrial-strength optimizers can be efficiently reduced to "anorexic" equivalents featuring only a few plans, without materially impacting query processing quality. Extensive experimentation with a rich set of TPC-H and TPC-DS-based query templates in a variety of database environments indicate that plan diagram reduction typically retains plans that are substantially resistant to selectivity errors on the base relations. However, it can sometimes also be severely counter-productive, with the replacements performing much worse. We address this problem through a generalized mathematical characterization of plan cost behavior over the parameter space, which lends itself to efficient criteria of when it is safe to reduce. Our strategies are fully non-invasive and have been implemented in the Picasso optimizer visualization tool.

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The applicability of Artificial Neural Networks for predicting the stress-strain response of jointed rocks at varied confining pressures, strength properties and joint properties (frequency, orientation and strength of joints) has been studied in the present paper. The database is formed from the triaxial compression tests on different jointed rocks with different confining pressures and different joint properties reported by various researchers. This input data covers a wide range of rock strengths, varying from very soft to very hard. The network was trained using a 3 layered network with feed forward back propagation algorithm. About 85% of the data was used for training and remaining15% for testing the predicting capabilities of the network. Results from the analyses were very encouraging and demonstrated that the neural network approach is efficient in capturing the complex stress-strain behaviour of jointed rocks. A single neural network is demonstrated to be capable of predicting the stress-strain response of different rocks, whose intact strength vary from 11.32 MPa to 123 MPa and spacing of joints vary from 10 cm to 100 cm for confining pressures ranging from 0 to 13.8 MPa.