Atomic ordering in nano-layered FePt: Multiscale Monte Carlo simulation

Nano- and meso-scale simulation of chemical ordering kinetics in nano-layered L1(0)-AB binary intermetallics was performed. In the nano- (atomistic) scale Monte Carlo (MC) technique with vacancy mechanism of atomic migration implemented with diverse models for the system energetics was used. The meso-scale microstructure evolution was, in turn, simulated by means of a MC procedure applied to a system built of meso-scale voxels ordered in particular L1(0) variants. The voxels were free to change the L1(0) variant and interacted with antiphase-boundary energies evaluated within the nano-scale simulations. The study addressed FePt thin layers considered as a material for ultra-high-density magnetic storage media and revealed metastability of the L1(0) c-variant superstructure with monoatomic planes parallel to the (001)-oriented layer surface and off-plane easy magnetization. The layers, originally perfectly ordered in the c-variant, showed discontinuous precipitation of a- and b-L1(0)-variant domains running in parallel with homogeneous disordering (i.e. generation of antisite defects). The domains nucleated heterogeneously on the free monoatomic Fe surface of the layer, grew inwards its volume and relaxed towards an equilibrium microstructure of the system. Two

On Entropy-Based Molecular Descriptors: Statistical Analysis of Real and Synthetic Chemical Structures

This paper presents an analysis of entropy-based molecular descriptors. Specifically, we use real chemical structures, as well as synthetic isomeric structures, and investigate properties of and among descriptors with respect to the used data set by a statistical analysis. Our numerical results provide evidence that synthetic chemical structures are notably different to real chemical structures and, hence, should not be used to investigate molecular descriptors. Instead, an analysis based on real chemical structures is favorable. Further, we find strong hints that molecular descriptors can be partitioned into distinct classes capturing complementary information.

Structural information content of networks: Graph entropy based on local vertex functionals

In this paper we define the structural information content of graphs as their corresponding graph entropy. This definition is based on local vertex functionals obtained by calculating-spheres via the algorithm of Dijkstra. We prove that the graph entropy and, hence, the local vertex functionals can be computed with polynomial time complexity enabling the application of our measure for large graphs. In this paper we present numerical results for the graph entropy of chemical graphs and discuss resulting properties. (C) 2007 Elsevier Ltd. All rights reserved.

Entanglement entropy dynamics of Heisenberg chains

By means of the time dependent density matrix renormalization group algorithm we study the zero-temperature dynamics of the Von Neumann entropy of a block of spins in a Heisenberg chain after a sudden quench in the anisotropy parameter. In the absence of any disorder the block entropy increases linearly with time and then saturates. We analyse the velocity of propagation of the entanglement as a function of the initial and final anisotropies and compare our results, wherever possible, with those obtained by means of conformal field theory. In the disordered case we find a slower ( logarithmic) evolution which may signal the onset of entanglement localization.

Performance enhancement of polymer nanocomposites via multiscale modelling of processing and properties

This paper provides an overview of research on modelling of the structure–property interactions of polymer nanocomposites in manufacturing processes (stretch blow moulding and thermoforming) involving large-strain biaxial stretching of relatively thin sheets, aimed at developing computer modelling tools to help producers of materials, product designers and manufacturers exploit these materials to the full, much more quickly than could be done by experimental methods alone. The exemplar systems studied are polypropylene and polyester terephalate, with nanoclays. These were compounded and extruded into 2mm thick sheet which was then biaxially stretched at 155°C for the PP and 90 to 100°C for the PET. Mechanical properties were determined for the unstretched and stretched materials, together with TEM and XRD studies of structure. Multi-scale modelling, using representative volume elements is used to model the properties of these products.

Multiscale simulation of nanometric cutting of single crystal copper and its experimental validation

In this paper a multiscale simulation study was carried out in order to gain in-depth understanding of machining mechanism of nanometric cutting of single crystal copper. This study was focused on the effects of crystal orientation and cutting direction on the attainable machined surface quality. The machining mechanics was analyzed through cutting forces, chip formation morphology, generation and evolution of defects and residual stresses on the machined surface. The simulation results showed that the crystal orientation of the copper material and the cutting direction significantly influenced the deformation mechanism of the workpiece materials during the machining process. Relatively lower cutting forces were experienced while selecting crystal orientation family {1 1 1}. Dislocation movements were found to concentrate in front of the cutting chip while cutting on the (1 1 1) surface along the View the MathML source cutting direction thus, resulting in much smaller damaged layer on the machined surface, compared to other orientations. This crystal orientation and cutting direction therefore recommended for nanometric cutting of single crystal copper in practical applications. A nano-scratching experiment was performed to validate the above findings.

Using entropy based mean shift filter and modified watershed transform for grain segmentation

Life science research aims to continuously improve the quality and standard of human life. One of the major challenges in this area is to maintain food safety and security. A number of image processing techniques have been used to investigate the quality of food products. In this paper,we propose a new algorithm to effectively segment connected grains so that each of them can be inspected in a later processing stage. One family of the existing segmentation methods is based on the idea of watersheding, and it has shown promising results in practice.However,due to the over-segmentation issue,this technique has experienced poor performance in various applications,such as inhomogeneous background and connected targets. To solve this problem,we present a combination of two classical techniques to handle this issue.In the first step,a mean shift filter is used to eliminate the inhomogeneous background, where entropy is used to be a converging criterion. Secondly,a color gradient algorithm is used in order to detect the most significant edges, and a marked watershed transform is applied to segment cluttered objects out of the previous processing stages. The proposed framework is capable of compromising among execution time, usability, efficiency and segmentation outcome in analyzing ring die pellets. The experimental results demonstrate that the proposed approach is effectiveness and robust.