Modeling LSPR nano-particles by using neural networks

Daryoush Mortazavi, Abbas Z. Kouzani, Ladislau Matekovits

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review


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.

Original languageEnglish
Title of host publicationBODYNETS 2014
Subtitle of host publicationProceedings of the 9th International Conference on Body Area Networks
EditorsGiancarlo Fortino, Junichi Suzuki, Yiannis Andreopoulos, Mehmet Yuce, Yang Hao, Raffaele Gravina
Place of PublicationBrussels, Belgium
PublisherEuropean Union Digital Library
Number of pages4
ISBN (Electronic)9781631900471
Publication statusPublished - 21 Nov 2014
Externally publishedYes
Event9th International Conference on Body Area Networks, BODYNETS 2014 - London, United Kingdom
Duration: 29 Sept 20141 Oct 2014


Other9th International Conference on Body Area Networks, BODYNETS 2014
Country/TerritoryUnited Kingdom


  • Artificial neural network
  • Localized surface plasmon resonance
  • Numerical method
  • Optimization


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