Robust FDTD Modeling of Graphene-Based Conductive Materials with Transient Features for Advanced Antenna Applications.
Pablo H Zapata CanoStamatios AmanatiadisZaharias D ZaharisTraianos V YioultsisPavlos I LaziridisNikolaos V KantartzisPublished in: Nanomaterials (Basel, Switzerland) (2023)
The accurate modeling of frequency-dispersive materials is a challenging task, especially when a scheme with a transient nature is utilized, as it is the case of the finite-difference time-domain method. In this work, a novel implementation for the modeling of graphene-oriented dispersive materials via the piecewise linear recursive convolution scheme, is introduced, while the time-varying conductivity feature is, additionally, launched. The proposed algorithm is employed to design a reduced graphene-oxide antenna operating at 6 GHz. The transient response to graphene's conductivity variations is thoroughly studied and a strategy to enhance the antenna performance by exploiting the time-varying graphene oxide is proposed. Finally, the use of the featured antenna for modern sensing applications is demonstrated through the real-time monitoring of voltage variation.
Keyphrases
- reduced graphene oxide
- energy transfer
- cerebral ischemia
- gold nanoparticles
- machine learning
- ionic liquid
- neural network
- room temperature
- solid phase extraction
- deep learning
- carbon nanotubes
- healthcare
- primary care
- gas chromatography mass spectrometry
- walled carbon nanotubes
- gas chromatography
- high resolution
- simultaneous determination
- solid state