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R. Tarparelli, R. Iovine, L. Spada, L. Vegni (2014)
Surface plasmon resonance of nanoshell particles with PMMA-graphene coreCompel-the International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 33
G. Hanson, A. Yakovlev, A. Mafi (2011)
Excitation of discrete and continuous spectrum for a surface conductivity model of grapheneJournal of Applied Physics, 110
K. Novoselov, SUPARNA DUTTASINHA, S. Morozov, D. Jiang, Y. Zhang, S. Dubonos, I. Grigorieva, A. Firsov (2004)
Electric Field Effect in Atomically Thin Carbon FilmsScience, 306
G. Steiner, D. Watzenig, C. Magele, U. Baumgartner (2005)
Statistical robust design using the unscented transformationCompel-the International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 24
Ashka Vakil, N. Engheta (2011)
Transformation Optics Using GrapheneScience, 332
A. Chiariello, C. Forestiere, G. Miano, A. Maffucci (2013)
Scattering properties of carbon nanotubesCompel-the International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 32
B. Nguyen, C. Furse, J. Simpson (2015)
A 3-D Stochastic FDTD Model of Electromagnetic Wave Propagation in Magnetized Ionosphere PlasmaIEEE Transactions on Antennas and Propagation, 63
Robert Edwards, Andrew Marvin, Stuart Porter (2010)
Uncertainty Analyses in the Finite-Difference Time-Domain MethodIEEE Transactions on Electromagnetic Compatibility, 52
L. Codecasa, L. Rienzo (2014)
Stochastic Finite Integration Technique Formulation for ElectrokineticsIEEE Transactions on Magnetics, 50
M. Fratila, R. Ramarotafika, A. Benabou, S. Clénet, Abdelmounaouim Tounzi (2013)
Stochastic post‐processing calculation of iron losses – application to a PMSMCompel-the International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 32
G. Hanson (2007)
Dyadic Green's functions and guided surface waves for a surface conductivity model of grapheneJournal of Applied Physics, 103
Frank Hastings, J. Schneider, S. Broschat (1995)
A Monte-Carlo FDTD technique for rough surface scatteringIEEE Transactions on Antennas and Propagation, 43
S. Smith, C. Furse (2012)
Stochastic FDTD for Analysis of Statistical Variation in Electromagnetic FieldsIEEE Transactions on Antennas and Propagation, 60
G. Bouzianas, N. Kantartzis, T. Yioultsis, T. Tsiboukis (2014)
Consistent Study of Graphene Structures Through the Direct Incorporation of Surface ConductivityIEEE Transactions on Magnetics, 50
Pai-Yen Chen, A. Alú (2011)
Atomically thin surface cloak using graphene monolayers.ACS nano, 5 7
S. Mikhailov, K. Ziegler (2007)
New electromagnetic mode in graphene.Physical review letters, 99 1
Ajibola Ajayi, Philip Ingrey, Phillip Sewell, Christos Christopoulos (2008)
Direct Computation of Statistical Variations in Electromagnetic ProblemsIEEE Transactions on Electromagnetic Compatibility, 50
V. Gusynin, S. Sharapov, J. Carbotte (2007)
Magneto-optical conductivity in grapheneJournal of Physics: Condensed Matter, 19
A. Taflove (1995)
Computational Electrodynamics the Finite-Difference Time-Domain Method
A. Austin, N. Sood, J. Siu, C. Sarris (2013)
Application of Polynomial Chaos to Quantify Uncertainty in Deterministic Channel ModelsIEEE Transactions on Antennas and Propagation, 61
PurposeImportant 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.Design/methodology/approachInitially, 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.FindingsThe 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.Originality/valueThe 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.
COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering – Emerald Publishing
Published: Sep 4, 2017
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