We show that the thermal and electrical properties of single wall carbon nanotube (CNT)-polymer composites are significantly enhanced by magnetic alignment during processing. The electrical transport properties of the composites are mainly governed by the hopping conduction with localization lengths comparable to bundle diameters. The bundling of nanotubes during the composite processing is an important factor for electrical, and in particular, for thermal transport properties. Better CNT isolation will be needed to reach the theoretical thermal conductivity limit for CNT composites.

Carbon nanotubes possess exceptional mechanical properties and superior thermal and electric properties.[1±4] Hence, nanotubes can be ideal reinforcement fibers for structural composites. For example, a cast composite film consisting of polystyrene and carbon nanotubes (5 % volume fraction) has increased the modulus by 100 % and the strength of the polystyrene by 25 %.[5] Moreover, the carbon nanotubes reinforcement increased the toughness of the composite by absorbing energy because of their high elastic behavior during loading.

This letter introduces a new algorithm for the restoration of a noisy blurred image based on the support vector regression (SVR). Experiments show that the performance of the SVR is very robust in blind image deconvolution where the types of blurs, point spread function (PSF) support, and noise level are all unknown.

The processing path model based on the conservation principle in the orientation space allows us to optimize processing path from a given initial state to a desired final microstructure for polycrystalline materials. This model uses texture coefficients in spherical harmonics expansion as descriptors to represent the texture state of polycrystalline materials. In this work, the effect of increasing the number of texture coefficients used in the series expansion (decided by l max ) on the prediction accuracy of texture evolution is investigated.Key words: Processing path, materials design, texture evolution, microstructure, property Das Processing-Path-Modell zur Gefügeentwicklung, das in dieser Studie entwickelt wurde, basiert auf dem Erhaltungsprinzip im Orientierungsraum und ermöglicht die endgültige Mikrostrukturaus den ursprünglichen Mikrostrukturdaten eines polykristallinen Materials -vorauszusagen. Um den Gefügestatus eines polykristallinen Materials zu beschreiben, verwendet dieses Modell zur Materialbeschreibung Gefügekoeffizienten in sphärisch-harmonischer Reihenentwicklung. In dieser Arbeit wurde die Auswirkung einer Erhöhung der Gefügekoeffizienten in der Reihenentwicklung (dargestellt durch l max ) auf die Voraussage des End-gefüges untersucht.