Modeling LSPR nano-particles by using neural networks

Daryoush Mortazavi; Abbas Z. Kouzani; Ladislau Matekovits

doi:10.4108/icst.bodynets.2014.257112

Modeling LSPR nano-particles by using neural networks

Daryoush Mortazavi, Abbas Z. Kouzani, Ladislau Matekovits

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	BODYNETS 2014
Subtitle of host publication	Proceedings of the 9th International Conference on Body Area Networks
Editors	Giancarlo Fortino, Junichi Suzuki, Yiannis Andreopoulos, Mehmet Yuce, Yang Hao, Raffaele Gravina
Place of Publication	Brussels, Belgium
Publisher	European Union Digital Library
Pages	316-319
Number of pages	4
ISBN (Electronic)	9781631900471
DOIs	https://doi.org/10.4108/icst.bodynets.2014.257112
Publication status	Published - 21 Nov 2014
Externally published	Yes
Event	9th International Conference on Body Area Networks, BODYNETS 2014 - London, United Kingdom Duration: 29 Sept 2014 → 1 Oct 2014

Other

Other	9th International Conference on Body Area Networks, BODYNETS 2014
Country/Territory	United Kingdom
City	London
Period	29/09/14 → 1/10/14

Keywords

Artificial neural network
Localized surface plasmon resonance
Numerical method
Optimization

Access to Document

10.4108/icst.bodynets.2014.257112

Cite this

Mortazavi, D., Kouzani, A. Z., & Matekovits, L. (2014). Modeling LSPR nano-particles by using neural networks. In G. Fortino, J. Suzuki, Y. Andreopoulos, M. Yuce, Y. Hao, & R. Gravina (Eds.), BODYNETS 2014: Proceedings of the 9th International Conference on Body Area Networks (pp. 316-319). European Union Digital Library. https://doi.org/10.4108/icst.bodynets.2014.257112

Mortazavi, Daryoush ; Kouzani, Abbas Z. ; Matekovits, Ladislau. / Modeling LSPR nano-particles by using neural networks. BODYNETS 2014: Proceedings of the 9th International Conference on Body Area Networks. editor / Giancarlo Fortino ; Junichi Suzuki ; Yiannis Andreopoulos ; Mehmet Yuce ; Yang Hao ; Raffaele Gravina. Brussels, Belgium : European Union Digital Library, 2014. pp. 316-319

@inproceedings{04e8d2f0321848a19dcdf2cc32b2a880,

title = "Modeling LSPR nano-particles by using neural networks",

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, Localized surface plasmon resonance, Numerical method, Optimization",

author = "Daryoush Mortazavi and Kouzani, {Abbas Z.} and Ladislau Matekovits",

year = "2014",

month = nov,

day = "21",

doi = "10.4108/icst.bodynets.2014.257112",

language = "English",

pages = "316--319",

editor = "Giancarlo Fortino and Junichi Suzuki and Yiannis Andreopoulos and Mehmet Yuce and Yang Hao and Raffaele Gravina",

booktitle = "BODYNETS 2014",

publisher = "European Union Digital Library",

note = "9th International Conference on Body Area Networks, BODYNETS 2014 ; Conference date: 29-09-2014 Through 01-10-2014",

}

Mortazavi, D, Kouzani, AZ & Matekovits, L 2014, Modeling LSPR nano-particles by using neural networks. in G Fortino, J Suzuki, Y Andreopoulos, M Yuce, Y Hao & R Gravina (eds), BODYNETS 2014: Proceedings of the 9th International Conference on Body Area Networks. European Union Digital Library, Brussels, Belgium, pp. 316-319, 9th International Conference on Body Area Networks, BODYNETS 2014, London, United Kingdom, 29/09/14. https://doi.org/10.4108/icst.bodynets.2014.257112

Modeling LSPR nano-particles by using neural networks. / Mortazavi, Daryoush; Kouzani, Abbas Z.; Matekovits, Ladislau.
BODYNETS 2014: Proceedings of the 9th International Conference on Body Area Networks. ed. / Giancarlo Fortino; Junichi Suzuki; Yiannis Andreopoulos; Mehmet Yuce; Yang Hao; Raffaele Gravina. Brussels, Belgium: European Union Digital Library, 2014. p. 316-319.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - Modeling LSPR nano-particles by using neural networks

AU - Mortazavi, Daryoush

AU - Kouzani, Abbas Z.

AU - Matekovits, Ladislau

PY - 2014/11/21

Y1 - 2014/11/21

N2 - 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.

AB - 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.

KW - Artificial neural network

KW - Localized surface plasmon resonance

KW - Numerical method

KW - Optimization

UR - http://www.scopus.com/inward/record.url?scp=84936015910&partnerID=8YFLogxK

U2 - 10.4108/icst.bodynets.2014.257112

DO - 10.4108/icst.bodynets.2014.257112

M3 - Conference proceeding contribution

AN - SCOPUS:84936015910

SP - 316

EP - 319

BT - BODYNETS 2014

A2 - Fortino, Giancarlo

A2 - Suzuki, Junichi

A2 - Andreopoulos, Yiannis

A2 - Yuce, Mehmet

A2 - Hao, Yang

A2 - Gravina, Raffaele

PB - European Union Digital Library

CY - Brussels, Belgium

T2 - 9th International Conference on Body Area Networks, BODYNETS 2014

Y2 - 29 September 2014 through 1 October 2014

ER -

Modeling LSPR nano-particles by using neural networks

Abstract

Other

Keywords

Access to Document

Other files and links

Fingerprint

Cite this