Influence of cluster-assembly parameters on the field emission properties of nanostructured carbon films

Supersonic cluster beam deposition has been used to produce films with different nanostructures by controlling the deposition parameters such as the film thickness, substrate temperature and cluster mass distribution. The field emission properties of cluster-assembled carbon films have been characterized and correlated to the evolution of the film nanostructure. Threshold fields ranging between 4 and 10 V/mum and saturation current densities as high as 0.7 mA have been measured for samples heated during deposition. A series of voltage ramps, i.e., a conditioning process, was found to initiate more stable and reproducible emission. It was found that the presence of graphitic particles (onions, nanotube embryos) in the films substantially enhances the field emission performance. Films patterned on a micrometer scale have been conditioned spot by spot by a ball-tip anode, showing that a relatively high emission site density can be achieved from the cluster-assembled material. (C) 2002 American Institute of Physics.

Characterisation of cell adhesion to substrate materials and the resistance to enzymatic and mechanical cell-removal

Cell-implant adhesive strength is important for prostheses. In this paper, an investigation is described into the adhesion of bovine chondrocytes to Ti6Al4V-based substrates with different surface roughnesses and compositions. Cells were cultured for 2 or 5 days, to promote adhesion. The ease of cell removal was characterised, using both biochemical (trypsin) and mechanical (accelerated buoyancy and liquid flow) methods. Computational fluid dynamics (CFD) modelling has been used to estimate the shear forces applied to the cells by the liquid flow. A comparison is presented between the ease of cell detachment indicated using these methods, for the three surfaces investigated. © 2008 Materials Research Society.

An approximate likelihood method for estimating static parameters in multi-target tracking models

This paper considers a class of dynamic Spatial Point Processes (PP) that evolves over time in a Markovian fashion. This Markov in time PP is hidden and observed indirectly through another PP via thinning, displacement and noise. This statistical model is important for Multi object Tracking applications and we present an approximate likelihood based method for estimating the model parameters. The work is supported by an extensive numerical study.