96 resultados para Hierarchical morphology
Resumo:
We report a novel, rapid, and low-temperature method for the synthesis of undoped and Eu-doped GdOOH spherical hierarchical structures, without using any structure-directing agents, through the microwave irradiation route. The as-prepared product consists of nearly monodisperse microspheres measuring about 1.3 mu m in diameter. Electron microscopy reveals that each microsphere is an assembly of two-dimensional nanoflakes (about 30 nm thin) which, in turn, result from the assembly of crystallites measuring about 9 nm in diameter. Thus, a three-level hierarchy can be seen in the formation of the GdOOH microspheres: from nanoparticles to 2D nanoflakes to 3D spherical structures. When doped with Eu3+ ions, the GdOOH microspheres show a strong red emission, making them promising candidates as phosphors. Finally, thermal conversion at modest temperatures leads to the formation of corresponding oxide structures with enhanced luminescence, while retaining the spherical morphology of their oxyhydroxide precursor.
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Dy-doped GdOOH microspherical structures were prepared in minutes without using any structure-directing agents, through the microwave irradiation route. The as-prepared product consists of nearly monodisperse sphere-like entities with each one representing a three-level hierarchy in its formation. Dy:GdOOH powder samples show a bright blue-green luminescence under UV excitation, making these structures potentially important in the field of optical and luminescent devices. Finally, thermal conversion to the corresponding oxide structures occurs at modest temperatures, spherical morphology intact and with enhanced luminescence behaviour. (C) 2014 Elsevier B.V. All rights reserved.
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We report the formation of dendritic hierarchical structures of alpha-Fe2O3 and nanostructures of Fe2O3 by the simple liquid-liquid interface method. The morphology of thin films determined by high-resolution scanning electron microscopy shows nanorods, nanosheets and dendritic Fe2O3. The identification of phases of iron oxide structures is carried out by using XRD and XPS studies. XRD and XPS measurements point out the highly crystalline dendritic alpha-Fe2O3 phase and the mixed phase of alpha- and gamma-Fe2O3 nanostructures. The magnetic measurement also suggests the presence of a mixed phase in the sample grown for 72 hours.
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The formation of the helical morphology in monolayers and bilayers of chiral amphiphilic assemblies is believed to be driven at least partly by the interactions at the chiral centers of the amphiphiles. However, a detailed microscopic understanding of these interactions and their relation with the helix formation is still not clear. In this article a study of the molecular origin of the chirality-driven helix formation is presented by calculating, for the first time, the effective pair potential between a pair of chiral molecules. This effective potential depends on the relative sizes of the groups attached to the two chiral centers, on the orientation of the amphiphile molecules, and also on the distance between them. We find that for the mirror-image isomers (in the racemic modification) the minimum energy conformation is a nearly parallel alignment of the molecules. On the other hand, the same for a pair of molecules of one kind of enantiomer favors a tilt angle between them, thus leading to the formation of a helical morphology of the aggregate. The tilt angle is determined by the size of the groups attached to the chiral centers of the pair of molecules considered and in many cases predicted it to be close to 45 degrees. The present study, therefore, provides a molecular origin of the intrinsic bending force, suggested by Helfrich (J. Chem. Phys. 1986, 85, 1085-1087), to be responsible for the formation of helical structure. This effective potential may explain many of the existing experimental results, such as the size and the concentration dependence of the formation of helical morphology. It is further found that the elastic forces can significantly modify the pitch predicted by the chiral interactions alone and that the modified real pitch is close to the experimentally observed value. The present study is expected to provide a starting point for future microscopic studies.
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We analyse the fault-tolerant parameters and topological properties of a hierarchical network of hypercubes. We take a close look at the Extended Hypercube (EH) and the Hyperweave (HW) architectures and also compare them with other popular architectures. These two architectures have low diameter and constant degree of connectivity making it possible to expand these networks without affecting the existing configuration. A scheme for incrementally expanding this network is also presented. We also look at the performance of the ASCEND/DESCEND class of algorithms on these architectures.
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A series of dual-phase (DP) steels containing finely dispersed martensite with different volume fractions of martensite (V-m) were produced by intermediate quenching of a boron- and vanadium-containing microalloyed steel. The volume fraction of martensite was varied from 0.3 to 0.8 by changing the intercritical annealing temperature. The tensile and impact properties of these steels were studied and compared to those of step-quenched steels, which showed banded microstructures. The experimental results show that DP steels with finely dispersed microstructures have excellent mechanical properties, including high impact toughness values, with an optimum in properties obtained at similar to 0.55 V-m. A further increase in V-m was found to decrease the yield and tensile strengths as well as the impact properties. It was shown that models developed on the basis of a rule of mixtures are inadequate in capturing the tensile properties of DP steels with V-m > 0.55. Jaoul-Crussard analyses of the work-hardening behavior of the high-martensite volume fraction DP steels show three distinct stages of plastic deformation.
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Learning automata arranged in a two-level hierarchy are considered. The automata operate in a stationary random environment and update their action probabilities according to the linear-reward- -penalty algorithm at each level. Unlike some hierarchical systems previously proposed, no information transfer exists from one level to another, and yet the hierarchy possesses good convergence properties. Using weak-convergence concepts it is shown that for large time and small values of parameters in the algorithm, the evolution of the optimal path probability can be represented by a diffusion whose parameters can be computed explicitly.
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Systems of learning automata have been studied by various researchers to evolve useful strategies for decision making under uncertainity. Considered in this paper are a class of hierarchical systems of learning automata where the system gets responses from its environment at each level of the hierarchy. A classification of such sequential learning tasks based on the complexity of the learning problem is presented. It is shown that none of the existing algorithms can perform in the most general type of hierarchical problem. An algorithm for learning the globally optimal path in this general setting is presented, and its convergence is established. This algorithm needs information transfer from the lower levels to the higher levels. Using the methodology of estimator algorithms, this model can be generalized to accommodate other kinds of hierarchical learning tasks.
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An approach is presented for hierarchical control of an ammonia reactor, which is a key unit process in a nitrogen fertilizer complex. The aim of the control system is to ensure safe operation of the reactor around the optimal operating point in the face of process variable disturbances and parameter variations. The four different layers perform the functions of regulation, optimization, adaptation, and self-organization. The simulation for this proposed application is conducted on an AD511 hybrid computer in which the AD5 analog processor is used to represent the process and the PDP-11/ 35 digital computer is used for the implementation of control laws. Simulation results relating to the different layers have been presented.
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A learning automaton operating in a random environment updates its action probabilities on the basis of the reactions of the environment, so that asymptotically it chooses the optimal action. When the number of actions is large the automaton becomes slow because there are too many updatings to be made at each instant. A hierarchical system of such automata with assured c-optimality is suggested to overcome that problem.The learning algorithm for the hierarchical system turns out to be a simple modification of the absolutely expedient algorithm known in the literature. The parameters of the algorithm at each level in the hierarchy depend only on the parameters and the action probabilities of the previous level. It follows that to minimize the number of updatings per cycle each automaton in the hierarchy need have only two or three actions.
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An algorithm is described for developing a hierarchy among a set of elements having certain precedence relations. This algorithm, which is based on tracing a path through the graph, is easily implemented by a computer.
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An algorithm is described for developing a hierarchy among a set of elements having certain precedence relations. This algorithm, which is based on tracing a path through the graph, is easily implemented by a computer.
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The term acclimation has been used with several connotations in the field of acclimatory physiology. An attempt has been made, in this paper, to define precisely the term “acclimation” for effective modelling of acclimatory processes. Acclimation is defined with respect to a specific variable, as cumulative experience gained by the organism when subjected to a step change in the environment. Experimental observations on a large number of variables in animals exposed to sustained stress, show that after initial deviation from the basal value (defined as “growth”), the variables tend to return to basal levels (defined as “decay”). This forms the basis for modelling biological responses in terms of their growth and decay. Hierarchical systems theory as presented by Mesarovic, Macko & Takahara (1970) facilitates modelling of complex and partially characterized systems. This theory, in conjunction with “growth-decay” analysis of biological variables, is used to model temperature regulating system in animals exposed to cold. This approach appears to be applicable at all levels of biological organization. Regulation of hormonal activity which forms a part of the temperature regulating system, and the relationship of the latter with the “energy” system of the animal of which it forms a part, are also effectively modelled by this approach. It is believed that this systematic approach would eliminate much of the current circular thinking in the area of acclimatory physiology.
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Microstructure and microtexture evolution during static annealing of a hot-extruded AZ21 magnesium alloy was studied. Apart from fine recrystallized equiaxed grains and large elongated deformed grains, a new third kind of abnormal grains that are stacked one after the other in a row parallel to the extrusion direction were observed. The crystallographic misorientation inside these grains was similar to that of the fine recrystallized grains. The large elongated grains exhibited significant in-grain misorientation. A self-consistent mechanistic model was developed to describe the formation of these grain morphologies during dynamic recrystallization (DRX). The texture of pre-extruded material, although lost in DRX, leaves a unique signature which manifests itself in the form of these grain morphologies. The origin of abnormal stacked grains was associated with slow nucleation in pre-extruded grains of a certain orientation. Further annealing resulted in large secondary recrystallized grains with occasional extension twins. (c) 2009 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
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α-Manganese dioxide is synthesized in a microemulsion medium by a redox reaction between KMnO4 and MnSO4 in presence of sodium dodecyl sulphate as a surface active agent. The morphology of MnO2 resembles nanopetals, which are spread parallel to the field. The material is further characterized by powder X-ray diffraction, energy dispersive analysis of X-ray, and Brunauer–Emmett–Teller surface area. Supercapacitance property of α-MnO2 nanopetals is studied by cyclic voltammetry and galvanostatic charge–discharge cycling. High values of specific capacitance are obtained.