Plasma nanoscience : from controlled complexity to practical simplicity

Plasma Nanoscience is a multidisciplinary research field which aims to elucidate the specific roles, purposes, and benefits of the ionized gas environment in assembling and processing nanoscale objects in natural, laboratory and technological situations. Compared to neutral gas-based routes, in low-temperature weakly-ionized plasmas there is another level of complexity related to the necessity of creating and sustaining a suitable degree of ionization and a much larger number of species generated in the gas phase. The thinner the nanotubes, the stronger is the quantum confinement of electrons and more unique size-dependent quantum effects can emerge. Furthermore, due to a very high mobility of electrons, the surfaces are at a negative potential compared to the plasma bulk. Therefore, there are non-uniform electric fields within the plasma sheath. The electric field lines start in the plasma bulk and converge to the sharp tips of the developing one-dimensional nanostructures.

Collective motion of dimers

We consider a discrete agent-based model on a one-dimensional lattice and a two-dimensional square lattice, where each agent is a dimer occupying two sites. Agents move by vacating one occupied site in favor of a nearest-neighbor site and obey either a strict simple exclusion rule or a weaker constraint that permits partial overlaps between dimers. Using indicator variables and careful probability arguments, a discrete-time master equation for these processes is derived systematically within a mean-field approximation. In the continuum limit, nonlinear diffusion equations that describe the average agent occupancy of the dimer population are obtained. In addition, we show that multiple species of interacting subpopulations give rise to advection-diffusion equations. Averaged discrete simulation data compares very well with the solution to the continuum partial differential equation models. Since many cell types are elongated rather than circular, this work offers insight into population-level behavior of collective cellular motion.

Multi-modal deformable medical image registration

Non-rigid image registration is an essential tool required for overcoming the inherent local anatomical variations that exist between images acquired from different individuals or atlases. Furthermore, certain applications require this type of registration to operate across images acquired from different imaging modalities. One popular local approach for estimating this registration is a block matching procedure utilising the mutual information criterion. However, previous block matching procedures generate a sparse deformation field containing displacement estimates at uniformly spaced locations. This neglects to make use of the evidence that block matching results are dependent on the amount of local information content. This paper presents a solution to this drawback by proposing the use of a Reversible Jump Markov Chain Monte Carlo statistical procedure to optimally select grid points of interest. Three different methods are then compared to propagate the estimated sparse deformation field to the entire image including a thin-plate spline warp, Gaussian convolution, and a hybrid fluid technique. Results show that non-rigid registration can be improved by using the proposed algorithm to optimally select grid points of interest.