Properties of grain boundaries in bulk, melt processed YB2Cu3O7-δ fabricated using bridge-shaped seeds

Single grain REBa2C3uO7 ((RE)BCO, where RE is a rare earth element or yttrium) bulk superconducting materials have significant potential for a variety of engineering applications due to their ability to trap high magnetic fields. However, it is well known that the presence of grain boundaries coupled with a high angle of misorientation (typically 5�) significantly reduces the critical current density, J c , in all forms of high temperature superconducting materials. It is of considerable fundamental and technological interest, therefore, to investigate the grain boundary properties of bulk, film and tape (RE)BCO. We report a successful multi-seeding technique for the fabrication of fully aligned, artificial (0��misalignment) grain boundaries within large grain YBCO bulk superconductors using bridge-shaped seeds. The microstructure and critical current densities of the grain boundaries produced by this technique have been studied in detail.

YBCO single grains seeded by 45° - 45° bridge-seeds of different lengths

Single grain, (RE)BCO bulk superconductors in large or complicated geometries are required for a variety of potential applications, such as motors and generators and magnetic shielding devices. As a result, top, multi-seeded, melt growth (TMSMG) has been investigated over the past two years in an attempt to enlarge the size of (RE)BCO single grains specifically for such applications. Of these multi-seeding techniques, so-called bridge seeding provides the best alignment of two seeds in a single grain growth process. Here we report, for the first time, the successful growth of YBCO using a special, 45{\deg} - 45{\deg}, arrangement of bridge-seeds. The superconducting properties, including trapped field, of the multi-seeded YBCO grains have been measured for different bridge lengths of the 45{\deg}- 45{\deg} bridge-seeds. The boundaries at the impinging growth front and the growth features of the top, multi-seeded surface and cross-section of the multi-seeded, samples have been analysed using optical microscopy. The results suggest that an impurity-free boundary between the two seeds of each leg of the bridge-seed can form when 45{\deg}- 45{\deg} bridge-seeds are used to enlarge the size of YBCO grains.

Enzyme-free glucose biosensor based on low density CNT forest grown directly on a Si/SiO2 substrate

A highly sensitive nonenzymatic amperometric glucose sensor was fabricated by using Ni nanoparticles homogeneously dispersed within and on the top of a vertically aligned CNT forest (CNT/Ni nanocomposite sensor), which was directly grown on a Si/SiO2 substrate. The surface morphology and elemental analysis were characterized using scanning electron microscopy and energy dispersive spectroscopy, respectively. Cyclic voltammetry and chronoamperometry were used to evaluate the catalytic activities of CNT/Ni electrode. The CNT/Ni nanocomposite sensor exhibited a great enhancement of anodic peak current after adding 5 mM glucose in alkaline solution. The sensor can also be applied to the quantification of glucose content with a linear range covering from 5 μM to 7 mM, a high sensitivity of 1433 μA mM-1 cm-2, and a low detection limit of 2 μM. The CNT/Ni nanocomposite sensor exhibits good reproducibility and long-term stability, moreover, it was also relatively insensitive to commonly interfering species, such as uric acid, ascorbic acid, acetaminophen, sucrose and d-fructose. © 2013 Elsevier B.V.

High contrast holograms using nanotube forest

Nanotube forest behaves as highly absorbent material when they are randomly placed in sub-wavelength scales. Furthermore, it is possible to create diffractive structures when these bulks are patterned in a substrate. Here, we introduce an alternative to fabricate intensity holograms by patterning fringes of nanotube forest on a substrate. The result is an efficient intensity hologram that is not restricted to sub-wavelength patterning. Both the theoretical and experimental analysis was performed with good agreement. The produced holograms show a uniform behaviour throughout the visible spectra. © 2013 AIP Publishing LLC.

Unconstrained monocular 3D human pose estimation by action detection and cross-modality regression forest

This work addresses the challenging problem of unconstrained 3D human pose estimation (HPE) from a novel perspective. Existing approaches struggle to operate in realistic applications, mainly due to their scene-dependent priors, such as background segmentation and multi-camera network, which restrict their use in unconstrained environments. We therfore present a framework which applies action detection and 2D pose estimation techniques to infer 3D poses in an unconstrained video. Action detection offers spatiotemporal priors to 3D human pose estimation by both recognising and localising actions in space-time. Instead of holistic features, e.g. silhouettes, we leverage the flexibility of deformable part model to detect 2D body parts as a feature to estimate 3D poses. A new unconstrained pose dataset has been collected to justify the feasibility of our method, which demonstrated promising results, significantly outperforming the relevant state-of-the-arts. © 2013 IEEE.

The Random Forest Kernel and other kernels for big data from random partitions

We present Random Partition Kernels, a new class of kernels derived by demonstrating a natural connection between random partitions of objects and kernels between those objects. We show how the construction can be used to create kernels from methods that would not normally be viewed as random partitions, such as Random Forest. To demonstrate the potential of this method, we propose two new kernels, the Random Forest Kernel and the Fast Cluster Kernel, and show that these kernels consistently outperform standard kernels on problems involving real-world datasets. Finally, we show how the form of these kernels lend themselves to a natural approximation that is appropriate for certain big data problems, allowing $O(N)$ inference in methods such as Gaussian Processes, Support Vector Machines and Kernel PCA.