The role of disorder on the AC and DC electrical conductivity of vapour grown carbon nanofibre/epoxy composites

Four dispersion methods were used for the preparation of vapour grown carbon nanofibre (VGCNF)/epoxy composites. It is shown that each method induces certain levels of VGCNF dispersion and distribution within the matrix, and that these have a strong influence on the composite electrical properties. A homogenous VGCNF dispersion does not necessarily imply higher electrical conductivity. In fact, it is concluded that the presence of well distributed clusters, rather than a fine dispersion, is more important for achieving larger conductivities for a given VGCNF concentration. It is also found that the conductivity can be described by a weak disorder regime.

One-sided Radial-Fundamental Matrix Estimation

For modern consumer cameras often approximate calibration data is available, making applications such as 3D reconstruction or photo registration easier as compared to the pure uncalibrated setting. In this paper we address the setting with calibrateduncalibrated image pairs: for one image intrinsic parameters are assumed to be known, whereas the second view has unknown distortion and calibration parameters. This situation arises e.g. when one would like to register archive imagery to recently taken photos. A commonly adopted strategy for determining epipolar geometry is based on feature matching and minimal solvers inside a RANSAC framework. However, only very few existing solutions apply to the calibrated-uncalibrated setting. We propose a simple and numerically stable two-step scheme to first estimate radial distortion parameters and subsequently the focal length using novel solvers. We demonstrate the performance on synthetic and real datasets.

The influence of matrix mediated hopping conductivity, filler concentration, aspect ratio and orientation on the electrical response of carbon nanotube/polymer nanocomposites

A model to simulate the conductivity of carbon nanotube/polymer nanocomposites is presented. The proposed model is based on hopping between the fillers. A parameter related to the influence of the matrix in the overall composite conductivity is defined. It is demonstrated that increasing the aspect ratio of the fillers will increase the conductivity. Finally, it is demonstrated that the alignment of the filler rods parallel to the measurement direction results in higher conductivity values, in agreement with results from recent experimental work.

The influence of the dispersion method on the electrical properties of vapor-grown carbon nanofiber/epoxy composites

The influence of the dispersion of vapor-grown carbon nanofibers (VGCNF) on the electrical properties of VGCNF/ Epoxy composites has been studied. A homogenous dispersion of the VGCNF does not imply better electrical properties. In fact, it is demonstrated that the most simple of the tested dispersion methods results in higher conductivity, since the presence of well-distributed nanofiber clusters appears to be a key factor for increasing composite conductivity.

Carbon Nanofiber Type and Content Dependence of the Physical Properties of Carbon Nanofiber Reinforced Polypropylene Composites

The variation of the physical properties of four differ- ent carbon nanofibers (CNFs), based-polymer nano- composites incorporated in the same polypropylene (PP) matrix by twin-screw extrusion process was investigated. Nanocomposites fabricated with CNFs with highly graphitic outer layer revealed electrical isolation-to-conducting behaviors as function of CNF’s content. Nanocomposites fabricated with CNFs with an outer layer consisting on a disordered pyro- litically stripped layer, in contrast, revealed better mechanical performance and enhanced thermal sta- bility. Further, CNF’s incorporation into the polymer increased the thermal stability and the degree of crystallinity of the polymer, independently on the filler content and type. In addition, dispersion of the CNFs’ clusters in PP was analyzed by transmitted light opti- cal microscopy, and grayscale analysis (GSA). The results showed a correlation between the filler concentration and the variance, a parameter which measures quantitatively the dispersion, for all composites. This method indicated a value of 1.4 vol% above which large clusters of CNFs cannot be dispersed effectively and as a consequence only slight changes in mechanical performance are observed. Finally, this study establishes that for tailoring the physical properties of CNF based-polymer nanocomposites, both adequate CNFs structure and content have to be chosen.

Eigenvalue computations in the context of data-sparse approximations of integral operators

In this work, we consider the numerical solution of a large eigenvalue problem resulting from a finite rank discretization of an integral operator. We are interested in computing a few eigenpairs, with an iterative method, so a matrix representation that allows for fast matrix-vector products is required. Hierarchical matrices are appropriate for this setting, and also provide cheap LU decompositions required in the spectral transformation technique. We illustrate the use of freely available software tools to address the problem, in particular SLEPc for the eigensolvers and HLib for the construction of H-matrices. The numerical tests are performed using an astrophysics application. Results show the benefits of the data-sparse representation compared to standard storage schemes, in terms of computational cost as well as memory requirements.