A novel method of extraction of blend component structure from SANS measurements of homopolymer bimodal blends

A new method is presented for the extraction of single-chain form factors and interchain interference functions from a range of small-angle neutron scattering (SANS) experiments on bimodal homopolymer blends. The method requires a minimum of three blends, made up of hydrogenated and deuterated components with matched degree of polymerization at two different chain lengths, but with carefully varying deuteration levels. The method is validated through an experimental study on polystyrene homopolymer bimodal blends with M A≈1/2MB. By fitting Debye functions to the structure factors, it is shown that there is good agreement between the molar mass of the components obtained from SANS and from chromatography. The extraction method also enables, for the first time, interchain scattering functions to be produced for scattering between chains of different lengths. © 2014 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Calculation of the electronic structure of carbon films using electron energy loss spectroscopy.

The detailed understanding of the electronic properties of carbon-based materials requires the determination of their electronic structure and more precisely the calculation of their joint density of states (JDOS) and dielectric constant. Low electron energy loss spectroscopy (EELS) provides a continuous spectrum which represents all the excitations of the electrons within the material with energies ranging between zero and about 100 eV. Therefore, EELS is potentially more powerful than conventional optical spectroscopy which has an intrinsic upper information limit of about 6 eV due to absorption of light from the optical components of the system or the ambient. However, when analysing EELS data, the extraction of the single scattered data needed for Kramers Kronig calculations is subject to the deconvolution of the zero loss peak from the raw data. This procedure is particularly critical when attempting to study the near-bandgap region of materials with a bandgap below 1.5 eV. In this paper, we have calculated the electronic properties of three widely studied carbon materials; namely amorphous carbon (a-C), tetrahedral amorphous carbon (ta-C) and C60 fullerite crystal. The JDOS curve starts from zero for energy values below the bandgap and then starts to rise with a rate depending on whether the material has a direct or an indirect bandgap. Extrapolating a fit to the data immediately above the bandgap in the stronger energy loss region was used to get an accurate value for the bandgap energy and to determine whether the bandgap is direct or indirect in character. Particular problems relating to the extraction of the single scattered data for these materials are also addressed. The ta-C and C60 fullerite materials are found to be direct bandgap-like semiconductors having a bandgaps of 2.63 and 1.59eV, respectively. On the other hand, the electronic structure of a-C was unobtainable because it had such a small bandgap that most of the information is contained in the first 1.2 eV of the spectrum, which is a region removed during the zero loss deconvolution.

Structure learning in action.

'Learning to learn' phenomena have been widely investigated in cognition, perception and more recently also in action. During concept learning tasks, for example, it has been suggested that characteristic features are abstracted from a set of examples with the consequence that learning of similar tasks is facilitated-a process termed 'learning to learn'. From a computational point of view such an extraction of invariants can be regarded as learning of an underlying structure. Here we review the evidence for structure learning as a 'learning to learn' mechanism, especially in sensorimotor control where the motor system has to adapt to variable environments. We review studies demonstrating that common features of variable environments are extracted during sensorimotor learning and exploited for efficient adaptation in novel tasks. We conclude that structure learning plays a fundamental role in skill learning and may underlie the unsurpassed flexibility and adaptability of the motor system.

Sparsity-Inducing Optimization Algorithm for the Extraction of Planar Structures in Noisy Point-Cloud Data

Most of the manual labor needed to create the geometric building information model (BIM) of an existing facility is spent converting raw point cloud data (PCD) to a BIM description. Automating this process would drastically reduce the modeling cost. Surface extraction from PCD is a fundamental step in this process. Compact modeling of redundant points in PCD as a set of planes leads to smaller file size and fast interactive visualization on cheap hardware. Traditional approaches for smooth surface reconstruction do not explicitly model the sparse scene structure or significantly exploit the redundancy. This paper proposes a method based on sparsity-inducing optimization to address the planar surface extraction problem. Through sparse optimization, points in PCD are segmented according to their embedded linear subspaces. Within each segmented part, plane models can be estimated. Experimental results on a typical noisy PCD demonstrate the effectiveness of the algorithm.