Modeling LSPR nano-particles by using neural networks

Daryoush Mortazavi; Abbas Kouzani; Ladislau Matekovits

9th International Conference on Body Area Networks

Research Article

Modeling LSPR nano-particles by using neural networks

Cite: BibTeX Plain Text

@INPROCEEDINGS{10.4108/icst.bodynets.2014.257112,
    author={Daryoush Mortazavi and Abbas Kouzani and Ladislau Matekovits},
    title={Modeling LSPR nano-particles by using neural networks},
    proceedings={9th International Conference on Body Area Networks},
    publisher={ICST},
    proceedings_a={BODYNETS},
    year={2014},
    month={11},
    keywords={artificial neural network optimization localized surface plasmon resonance numerical method},
    doi={10.4108/icst.bodynets.2014.257112}
}

Daryoush Mortazavi
Abbas Kouzani
Ladislau Matekovits
Year: 2014
Modeling LSPR nano-particles by using neural networks
BODYNETS
ACM
DOI: 10.4108/icst.bodynets.2014.257112

Daryoush Mortazavi¹, Abbas Kouzani¹, Ladislau Matekovits²^,*

1: School of Engineering, Deakin University, Geelong, Victoria 3216, Australia
2: Dept. of Electronics and Telecomunications, Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129 - Torino, Italy

*Contact email: ladislau.matekovits@polito.it

Abstract

Localized surface plasmon resonance (LSPR) biosensors represent a relatively new and hot research topic in biosensing applications. Since the fabrication of LSPR biosensors is time consuming and costly, providing a mathematical model that can predict the LSPR characteristics before any fabrication is on edge. Implementing such a model for the LSPR devices, and then optimally designing the LSPR geometrical parameters for a particular surface enhanced Raman Scattering (SERS) biosensor function is the concept that has not been explored yet. In this paper, a multi layered artificial neural network (ANN) is proposed which produces a mathematical model representing the characteristics of LSPR devices as a function of their physical dimensions for a specific shape of nano-particles. Such a model can be used to identify a LSPR structure that is appropriate for a biosensing application requiring specific LSPR characteristics. The numerical electromagnetic modeling approach of the finite difference time domain (FDTD) method, and the analytical method of electrostatic eigenmode are used to implement the proposed model.

Keywords: artificial neural network optimization localized surface plasmon resonance numerical method

Published: 2014-11-21
Publisher: ICST

: http://dx.doi.org/10.4108/icst.bodynets.2014.257112