Study of embedded lead-tin nanoparticles in aluminum matrix

Nanoembedded lead-tin alloys in aluminum matrix were synthesized by rapid solidification processing. These melt-spun aluminum alloys were then investigated using XRD, EDX and TEM. The XRD study reveals that the melt-spun samples contain elemental aluminum, lead and tin. The TEM analysis shows that embedded particles in aluminium matrix have a distinct two-phase contrast of lead and tin. The lead and tin in these nanoalloys exhibit an orientation relationship with the matrix aluminum and with each other. DSC studies were conducted to reveal the melting and solidification characteristics of these embedded nanoalloys. DSC thermograms exhibit features of multiple solidification exotherms on thermal cycling, which can be attributed to sequential melting and solidification of lead and tin in the respective alloys.

The production of AlN-rich matrix composites by the reactive infiltration of Al alloys in nitrogen

Aluminium nitride (AlN)-Al matrices reinforced with Al2O3 particulate have been fabricated by reactive infiltration of Al-2% Mg alloy into Al2O3 preforms in N-2 in the temperature range of 900-1075 degreesC. The growth of composites of useful thickness was facilitated by the presence of a Mg-rich external getter, in the absence of which composite growth is self-limiting and terminates prematurely. Successful growth of composites has been attributed to the reduction in residual oxygen partial pressure brought about by the reaction with oxygen of highly volatile Mg in the getter alloy. The microstructure of the matrix consists of AlN-rich regions contiguous with the particulate with metal-rich channels in-between, thereby suggesting that nitridation initiates by preferential wicking of alloy along the particle surfaces. The increase in nitride content of the matrix with temperature is consistent with hardness values that vary between similar to3 and 10 GPa. (C) 2002 Acta Materialia Inc. Published by Elsevier Science Ltd. All rights reserved.

density matrix renormalization group study of polyacene

We study the nature of excited states of long polyacene oligomers within a Pariser-Parr-Pople (PPP) Hamiltonian using the Symmetrized Density Matrix Renormalization Group (SDMRG) technique. We find a crossover between the two-photon state and the lowest dipole allowed excited state as the system size is increased from tetracene to pentacene. The spin-gap is the smallest gap. We also study the equilibrium geome tries in the ground and excited states from bond orders and bond-bond correlation functions. We find that the Peierls instability in the ground state of polyacene is conditional both from energetics and structure factors computed froth correlation functions.

Influence of matrix characteristics on fracture toughness of high volume fraction Al2O3/Al–AlN composites

The role of matrix microstructure on the fracture of Al-alloy composites with 60 vol% alumina particulates was studied. The matrix composition and microstructure were systematically varied by changing the infiltration temperature and heat treatment. Characterization was carried out by a combination of metallography, hardness measurements, and fracture studies conducted on compact tension specimens to study the fracture toughness and crack growth in the composites. The composites showed a rise in crack resistance with crack extension (R curves) due to bridges of intact matrix ligaments formed in the crack wake. The steady-state or plateau toughness reached upon stable crack growth was observed to be more sensitive to the process temperature rather than to the heat treatment. Fracture in the composites was predominantly by particle fracture, extensive deformation, and void nucleation in the matrix. Void nucleation occurred in the matrix in the as-solutionized and peak-aged conditions and preferentially near the interface in the underaged and overaged conditions. Micromechanical models based on crack bridging by intact ductile ligaments were modified by a plastic constraint factor from estimates of the plastic zone formed under indentations, and are shown to be adequate in predicting the steady-state toughness of the composite.

Right-Definite Multiparameter Sturm–Liouville Problems With Eigenparameter-Dependent Boundary Conditions

We study a system of ordinary differential equations linked by parameters and subject to boundary conditions depending on parameters. We assume certain definiteness conditions on the coefficient functions and on the boundary conditions that yield, in the corresponding abstract setting, a right-definite case. We give results on location of the eigenvalues and oscillation of the eigenfunctions.

Self-organization of multiple components in a steroidal hydrogel matrix: design, construction and studies on novel tunable luminescent gels and xerogels

The present work combines two rapidly growing research areas-functional supramolecular gels and lanthanide based hybrid materials. Facile hydrogel formation from several lanthanide(III) cholates has been demonstrated. The morphological and mechanical properties of these cholate gels were investigated by TEM and rheology. The hydrogel matrix was subsequently utilized for the sensitization of Tb(III) by doping a non-coordinating chromophore, 2,3-dihydroxynaphthalene (DHN), at micromolar concentrations. In the mixed gels of Tb(III)-Eu(III), an energy transfer pathway was found to operate from Tb(III) to Eu(III) and by utilizing this energy transfer, tunable multiple-color luminescent hydrogels were obtained. The emissive properties of the hydrogels were also retained in the xerogels and their suspensions in n-hexane were used for making luminescent coating on glass surface.

Dynamic matrix rank with partial lookahead

We consider the problem of maintaining information about the rank of a matrix $M$ under changes to its entries. For an $n \times n$ matrix $M$, we show an amortized upper bound of $O(n^{\omega-1})$ arithmetic operations per change for this problem, where $\omega < 2.376$ is the exponent for matrix multiplication, under the assumption that there is a {\em lookahead} of up to $\Theta(n)$ locations. That is, we know up to the next $\Theta(n)$ locations $(i_1,j_1),(i_2,j_2),\ldots,$ whose entries are going to change, in advance; however we do not know the new entries in these locations in advance. We get the new entries in these locations in a dynamic manner.

Continuous-time Single Network Adaptive Critic for Regulator Design of Nonlinear Control Affine Systems

An optimal control law for a general nonlinear system can be obtained by solving Hamilton-Jacobi-Bellman equation. However, it is difficult to obtain an analytical solution of this equation even for a moderately complex system. In this paper, we propose a continuoustime single network adaptive critic scheme for nonlinear control affine systems where the optimal cost-to-go function is approximated using a parametric positive semi-definite function. Unlike earlier approaches, a continuous-time weight update law is derived from the HJB equation. The stability of the system is analysed during the evolution of weights using Lyapunov theory. The effectiveness of the scheme is demonstrated through simulation examples.

Maximum Margin Classifiers with Specified False Positive and False Negative Error Rates

This paper addresses the problem of maximum margin classification given the moments of class conditional densities and the false positive and false negative error rates. Using Chebyshev inequalities, the problem can be posed as a second order cone programming problem. The dual of the formulation leads to a geometric optimization problem, that of computing the distance between two ellipsoids, which is solved by an iterative algorithm. The formulation is extended to non-linear classifiers using kernel methods. The resultant classifiers are applied to the case of classification of unbalanced datasets with asymmetric costs for misclassification. Experimental results on benchmark datasets show the efficacy of the proposed method.

Structural Alignment based Kernels for Protein Structure Classification

Structural alignments are the most widely used tools for comparing proteins with low sequence similarity. The main contribution of this paper is to derive various kernels on proteins from structural alignments, which do not use sequence information. Central to the kernels is a novel alignment algorithm which matches substructures of fixed size using spectral graph matching techniques. We derive positive semi-definite kernels which capture the notion of similarity between substructures. Using these as base more sophisticated kernels on protein structures are proposed. To empirically evaluate the kernels we used a 40% sequence non-redundant structures from 15 different SCOP superfamilies. The kernels when used with SVMs show competitive performance with CE, a state of the art structure comparison program.