Fabrication of multiwalled carbon nanotube bridges by poly-methylmethacrylate suspended dispersion

Poly-methylmethacrylate suspended dispersion was used to fabricate multiwalled carbon nanotube (MWCNT) bridges. Using this technique, nanotubes could be suspended between metal electrodes without any chemical etching of the substrate. The electrical measurement on suspended MWCNT bridges shows that the room temperature resistance ranges from under a kω to a few Mω.

Study of multi-walled carbon nanotube structures fabricated by PMMA suspended dispersion

We report on the electrical characteristics of plasma enhanced chemical vapour deposition (PECVD)-grown, multi-walled carbon nanotube (MWCNT) devices made by a new fabrication method, PMMA suspended dispersion. This method makes it possible to suspend nanotubes between metal electrodes and to remove unwanted nanotubes from the substrate. The measurements show that the MWCNTs are metallic and able to maintain a current density ∼2×106 A/cm2 for more than 15 days with a maximum current density of ∼1.8×107 A/cm2. This high current density and reliability will make PECVD-grown MWCNTs applicable to field emission cathodes. © 2002 Elsevier Science B.V. All rights reserved.

Uniform stability of a particle approximation of the optimal filter derivative

Sequential Monte Carlo methods, also known as particle methods, are a widely used set of computational tools for inference in non-linear non-Gaussian state-space models. In many applications it may be necessary to compute the sensitivity, or derivative, of the optimal filter with respect to the static parameters of the state-space model; for instance, in order to obtain maximum likelihood model parameters of interest, or to compute the optimal controller in an optimal control problem. In Poyiadjis et al. [2011] an original particle algorithm to compute the filter derivative was proposed and it was shown using numerical examples that the particle estimate was numerically stable in the sense that it did not deteriorate over time. In this paper we substantiate this claim with a detailed theoretical study. Lp bounds and a central limit theorem for this particle approximation of the filter derivative are presented. It is further shown that under mixing conditions these Lp bounds and the asymptotic variance characterized by the central limit theorem are uniformly bounded with respect to the time index. We demon- strate the performance predicted by theory with several numerical examples. We also use the particle approximation of the filter derivative to perform online maximum likelihood parameter estimation for a stochastic volatility model.

A backward particle interpretation of Feynman-Kac formulae

We design a particle interpretation of Feynman-Kac measures on path spaces based on a backward Markovian representation combined with a traditional mean field particle interpretation of the flow of their final time marginals. In contrast to traditional genealogical tree based models, these new particle algorithms can be used to compute normalized additive functionals "on-the-fly" as well as their limiting occupation measures with a given precision degree that does not depend on the final time horizon. We provide uniform convergence results with respect to the time horizon parameter as well as functional central limit theorems and exponential concentration estimates. Our results have important consequences for online parameter estimation for non-linear non-Gaussian state-space models. We show how the forward filtering backward smoothing estimates of additive functionals can be computed using a forward only recursion.