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Nowadays, nano-antennas or nanoscale absorbers made by innovative materials such as carbon nanotubes are gaining more and more interest, because of their outstanding features. The purpose of this paper is to investigate the scattering properties of carbon nanotubes, either isolated or arranged in arrays. The peculiar behaviour of such innovative materials is studied, taking also into account the finite length of the structure and the dependence of the scattering field from the operating temperature.

First a model is presented for the electrical transport along the carbon nanotubes, based on Boltzmann quasi-classical transport theory. The model includes quantistic and inertial phenomena observed in the carbon nanotube electrodynamics. The model also includes the effects of temperature. Using this electrodynamical model, the electromagnetic formulation of the scattering problem is cast in terms of a Pocklington-like equation. The numerical solution is obtained by means of the Galerkin method, with special care in handling the logarithmic singularity of the kernel. Case studies are carried out, either referred to isolated single-wall carbon nanotubes (SWCNTs) and array of SWCNTs.

The scattering properties of SWCNT are strongly influenced by the temperature and by the distance between the tubes. As temperature increases, the amplitude of the resonance peaks decreases, at a rate which is double the rate of changes of temperature. The resonance frequencies are insensitive to temperature. As for the distance between the tubes in an array, it influence the scattering resonance introducing a shift in the resonance frequencies which is appreciable for distances lower than the semi-length of the CNT. For higher distances the CNT scattered field may be regarded as the sum of the fields emitted by each CNT, as if they were isolated.

As far as now only SWCNTs have been studied. The multi-wall carbon nanotubes would show a richer behaviour with temperature, due to the joint effect of reduction of the mean free path and increase of the number of conducting channels, as temperature increases.

Possible use of carbon nanotubes as absorbing material or scatterers.

The model presented here is based on a self-consistent and physically meaningful description of the CNT electrodynamics, which takes rigorously into account the effect of temperature, size and chirality of each CNT.

Important statistical variations are likely to appear in the propagation of surface plasmon polariton waves atop the surface of graphene sheets, degrading the expected performance of real-life THz applications. This paper aims to introduce an efficient numerical algorithm that is able to accurately and rapidly predict the influence of material-based uncertainties for diverse graphene configurations.

Initially, the surface conductivity of graphene is described at the far infrared spectrum and the uncertainties of its main parameters, namely, the chemical potential and the relaxation time, on the propagation properties of the surface waves are investigated, unveiling a considerable impact. Furthermore, the demanding two-dimensional material is numerically modeled as a surface boundary through a frequency-dependent finite-difference time-domain scheme, while a robust stochastic realization is accordingly developed.

The mean value and standard deviation of the propagating surface waves are extracted through a single-pass simulation in contrast to the laborious Monte Carlo technique, proving the accomplished high efficiency. Moreover, numerical results, including graphene’s surface current density and electric field distribution, indicate the notable precision, stability and convergence of the new graphene-based stochastic time-domain method in terms of the mean value and the order of magnitude of the standard deviation.

The combined uncertainties of the main parameters in graphene layers are modeled through a high-performance stochastic numerical algorithm, based on the finite-difference time-domain method. The significant accuracy of the numerical results, compared to the cumbersome Monte Carlo analysis, renders the featured technique a flexible computational tool that is able to enhance the design of graphene THz devices due to the uncertainty prediction.