Effects of pre-treatment and plasma enhancement on chemical vapor deposition of carbon nanotubes from ultra-thin catalyst films

We report a detailed study of surface-bound chemical vapor deposition of carbon nanotubes and nanofibers from evaporated transition metal catalysts exposed to ammonia diluted acetylene. We show that a reduction of the Fe/Co catalyst film thickness below 3 nm results into a transition from large diameter (> 40 nm), bamboo-like nanofibers to small diameter (similar to 5 nm) multi-walled carbon nanotubes. The nanostructuring of ultrathin catalyst films critically depends on the gas atmosphere, with the resulting island distribution initiating the carbon nucleation. Compared to purely thermal chemical vapor deposition, we find that, for small diameter nanotube growth, DC plasma assistance is detrimental to graphitization and sample homogeneity and cannot prevent an early catalyst poisoning. (c) 2006 Elsevier B.V. All rights reserved.

Multi-channel signal separation

The separation of independent sources from mixed observed data is a fundamental and challenging problem. In many practical situations, observations may be modelled as linear mixtures of a number of source signals, i.e. a linear multi-input multi-output system. A typical example is speech recordings made in an acoustic environment in the presence of background noise and/or competing speakers. Other examples include EEG signals, passive sonar applications and cross-talk in data communications. In this paper, we propose iterative algorithms to solve the n × n linear time invariant system under two different constraints. Some existing solutions for 2 × 2 systems are reviewed and compared.

Robust modelling of noisy ARMA signals

In this paper methods are developed for enhancement and analysis of autoregressive moving average (ARMA) signals observed in additive noise which can be represented as mixtures of heavy-tailed non-Gaussian sources and a Gaussian background component. Such models find application in systems such as atmospheric communications channels or early sound recordings which are prone to intermittent impulse noise. Markov Chain Monte Carlo (MCMC) simulation techniques are applied to the joint problem of signal extraction, model parameter estimation and detection of impulses within a fully Bayesian framework. The algorithms require only simple linear iterations for all of the unknowns, including the MA parameters, which is in contrast with existing MCMC methods for analysis of noise-free ARMA models. The methods are illustrated using synthetic data and noise-degraded sound recordings.