A simple combinatorial method aiding research on single-walled carbon nanotube growth on substrates

Establishing fabrication methods of carbon nanotubes (CNTs) is essential to realize many applications expected for CNTs. Catalytic growth of CNTs on substrates by chemical vapor deposition (CVD) is promising for direct fabrication of CNT devices, and catalyst nanoparticles play a crucial role in such growth. We have developed a simple method called "combinatorial masked deposition (CMD)", in which catalyst particles of a given series of sizes and compositions are formed on a single substrate by annealing gradient catalyst layers formed by sputtering through a mask. CMD enables preparation of hundreds of catalysts on a wafer, growth of single-walled CNTs (SWCNTs), and evaluation of SWCNT diameter distributions by automated Raman mapping in a single day. CMD helps determinations of the CVD and catalyst windows realizing millimeter-tall SWCNT forest growth in 10 min, and of growth curves for a series of catalysts in a single measurement when combined with realtime monitoring. A catalyst library prepared using CMD yields various CNTs, ranging from individuals, networks, spikes, and to forests of both SWCNTs and multi-walled CNTs, and thus can be used to efficiently evaluate self-organized CNT field emitters, for example. The CMD method is simple yet effective for research of CNT growth methods. © 2010 The Japan Society of Applied Physics.

Microheterogeneous Thoria-Urania Fuels for Pressurized Water Reactors

A thorium-based fuel cycle for light water reactors will reduce the plutonium generation rate and enhance the proliferation resistance of the spent fuel. However, priming the thorium cycle with ²³⁵U is necessary, and the ²³⁵U fraction in the uranium must be limited to below 20% to minimize proliferation concerns. Thus, a once-through thorium-uranium dioxide (ThO2-UO2) fuel cycle of no less than 25% uranium becomes necessary for normal pressurized water reactor (PWR) operating cycle lengths. Spatial separation of the uranium and thorium parts of the fuel can improve the achievable burnup of the thorium-uranium fuel designs through more effective breeding of ²³³U from the ²³²Th. Focus is on microheterogeneous fuel designs for PWRs, where the spatial separation of the uranium and thorium is on the order of a few millimetres to a few centimetres, including duplex pellet, axially microheterogeneous fuel, and a checkerboard of uranium and thorium pins. A special effort was made to understand the underlying reactor physics mechanisms responsible for enhancing the achievable burnup at spatial separation of the two fuels. The neutron spectral shift was identified as the primary reason for the enhancement of burnup capabilities. Mutual resonance shielding of uranium and thorium is also a factor; however, it is small in magnitude. It is shown that the microheterogeneous fuel can achieve higher burnups, by up to 15%, than the reference all-uranium fuel. However, denaturing of the ²³³U in the thorium portion of the fuel with small amounts of uranium significantly impairs this enhancement. The denaturing is also necessary to meet conventional PWR thermal limits by improving the power share of the thorium region at the beginning of fuel irradiation. Meeting thermal-hydraulic design requirements by some of the microheterogeneous fuels while still meeting or exceeding the burnup of the all-uranium case is shown to be potentially feasible. However, the large power imbalance between the uranium and thorium regions creates several design challenges, such as higher fission gas release and cladding temperature gradients. A reduction of plutonium generation by a factor of 3 in comparison with all-uranium PWR fuel using the same initial ²³⁵U content was estimated. In contrast to homogeneously mixed U-Th fuel, microheterogeneous fuel has a potential for economic performance comparable to the all-UO2 fuel provided that the microheterogeneous fuel incremental manufacturing costs are negligibly small.

Systematic misregistration and the statistical analysis of surface data.

Spatial normalisation is a key element of statistical parametric mapping and related techniques for analysing cohort statistics on voxel arrays and surfaces. The normalisation process involves aligning each individual specimen to a template using some sort of registration algorithm. Any misregistration will result in data being mapped onto the template at the wrong location. At best, this will introduce spatial imprecision into the subsequent statistical analysis. At worst, when the misregistration varies systematically with a covariate of interest, it may lead to false statistical inference. Since misregistration generally depends on the specimen's shape, we investigate here the effect of allowing for shape as a confound in the statistical analysis, with shape represented by the dominant modes of variation observed in the cohort. In a series of experiments on synthetic surface data, we demonstrate how allowing for shape can reveal true effects that were previously masked by systematic misregistration, and also guard against misinterpreting systematic misregistration as a true effect. We introduce some heuristics for disentangling misregistration effects from true effects, and demonstrate the approach's practical utility in a case study of the cortical bone distribution in 268 human femurs.