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Modeling LSPR nano-particles by using neural networks
conference contribution
posted on 2014-11-21, 00:00 authored by Daryoush Mortazavi, Abbas KouzaniAbbas Kouzani, L MatekovitsLocalized 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.
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Event
Body Area Networks. Conference (9th : 2014 : London, Great Britain)Pagination
316 - 319Publisher
ACMLocation
London, Great BritainPublisher DOI
Start date
2014-09-29End date
2014-10-01ISBN-13
9781631900471Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2014–2017 ICSTTitle of proceedings
BodyNets 2014 : Proceedings of the Body Area Networks 2014 ConferenceUsage metrics
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