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The time response of plasmonic sensors due to binary adsorption: analytical versus numerical modeling

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2020
BinaryAdsorptionModeling.pdf (1.678Mb)
Authors
Jakšić, Olga
Jokić, Ivana
Jakšić, Zoran
Mladenović, Ivana
Radulović, Katarina
Frantlović, Miloš
Article (Accepted Version)
,
Springer Nature
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Abstract
In order to allow for multiscale modeling of complex systems we focus on various approaches to modeling binary adsorption. We consider multiple methods of modeling the temporal response of general plasmonic sensors. We start from the analytical approach. The kinetics of adsorption and desorption is modeled both as a first order reaction and as a second order reaction. The criteria for their validity and the choice between them in the case of two-component adsorption are established. Due to the nonlinearities of the second order reactions and the lack of their analytical solutions, computer aided modeling is considered next, also in multiple ways: the employment of numerical solvers, fitting of experimental results, the stochastic simulation algorithms and the employment of artificial neural networks (ANN). The examples we present illustrate the advantages and disadvantages of the particular approaches. The goal is to aid the concurrent multiscale modeling of adsorption-based devices. ...Machine learning in ANN performed here is used to estimate the equilibrium values of adsorbed quantities. The obtained results show that to train an ANN for the estimation of the equilibrium adsorption quantities the Levenberg-Marquardt and the Bayesian regularization algorithms are less efficient than the quasi-Newton BFGS (Broyden-Fletcher-Goldfarb-Shanno) algorithm.

Keywords:
adsorption kinetics / machine learning algorithms / plasmonic sensing / stochastic simulation algorithms
Source:
Applied Physics A, 2020, 126, 342-
Publisher:
  • Springer Nature
Funding / projects:
  • Micro- Nanosystems and Sensors for Electric Power and Process Industry and Environmental Protection (RS-32008)
Note:
  • This is a post-peer-review, pre-copyedit version of an article (Jakšić, O., Jokić, I., Jakšić, Z. et al. The time response of plasmonic sensors due to binary adsorption: analytical versus numerical modeling) published in Applied Physics A: Materials Science and Processing (Appl. Phys. A 126, 342, 2020) The final authenticated version is available online at: https://doi.org/10.1007/s00339-020-03524-3
  • The published version: http://cer.ihtm.bg.ac.rs/handle/123456789/3494

DOI: 10.1007/s00339-020-03524-3

ISSN: 0947-8396

WoS: 000526931900003

Scopus: 2-s2.0-85083375372
[ Google Scholar ]
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URI
https://cer.ihtm.bg.ac.rs/handle/123456789/3493
Collections
  • Radovi istraživača / Researchers' publications
Institution/Community
IHTM
TY  - JOUR
AU  - Jakšić, Olga
AU  - Jokić, Ivana
AU  - Jakšić, Zoran
AU  - Mladenović, Ivana
AU  - Radulović, Katarina
AU  - Frantlović, Miloš
PY  - 2020
UR  - https://cer.ihtm.bg.ac.rs/handle/123456789/3493
AB  - In order to allow for multiscale modeling of complex systems we focus on various approaches to modeling binary adsorption. We consider multiple methods of  modeling the temporal response of general plasmonic sensors. We start from the analytical approach. The kinetics of adsorption and desorption is modeled both as a first order reaction and as a second order reaction. The criteria for their validity and the choice between them in the case of two-component adsorption are established. Due to the nonlinearities of the second order reactions and the lack of their analytical solutions, computer aided modeling is considered next, also in multiple ways: the employment of numerical solvers, fitting of experimental results, the stochastic simulation algorithms and the employment of artificial neural networks (ANN). The examples we present illustrate the advantages and disadvantages of the particular approaches. The goal is to aid the concurrent multiscale modeling of adsorption-based devices. Machine learning in ANN performed here is used to estimate the equilibrium values of adsorbed quantities. The obtained results show that to train an ANN for the estimation of the equilibrium adsorption quantities the Levenberg-Marquardt and the Bayesian regularization algorithms are less efficient than the quasi-Newton BFGS (Broyden-Fletcher-Goldfarb-Shanno) algorithm.
PB  - Springer Nature
T2  - Applied Physics A
T1  - The time response of plasmonic sensors due to binary adsorption: analytical versus numerical modeling
VL  - 126
SP  - 342
DO  - 10.1007/s00339-020-03524-3
ER  - 
@article{
author = "Jakšić, Olga and Jokić, Ivana and Jakšić, Zoran and Mladenović, Ivana and Radulović, Katarina and Frantlović, Miloš",
year = "2020",
abstract = "In order to allow for multiscale modeling of complex systems we focus on various approaches to modeling binary adsorption. We consider multiple methods of  modeling the temporal response of general plasmonic sensors. We start from the analytical approach. The kinetics of adsorption and desorption is modeled both as a first order reaction and as a second order reaction. The criteria for their validity and the choice between them in the case of two-component adsorption are established. Due to the nonlinearities of the second order reactions and the lack of their analytical solutions, computer aided modeling is considered next, also in multiple ways: the employment of numerical solvers, fitting of experimental results, the stochastic simulation algorithms and the employment of artificial neural networks (ANN). The examples we present illustrate the advantages and disadvantages of the particular approaches. The goal is to aid the concurrent multiscale modeling of adsorption-based devices. Machine learning in ANN performed here is used to estimate the equilibrium values of adsorbed quantities. The obtained results show that to train an ANN for the estimation of the equilibrium adsorption quantities the Levenberg-Marquardt and the Bayesian regularization algorithms are less efficient than the quasi-Newton BFGS (Broyden-Fletcher-Goldfarb-Shanno) algorithm.",
publisher = "Springer Nature",
journal = "Applied Physics A",
title = "The time response of plasmonic sensors due to binary adsorption: analytical versus numerical modeling",
volume = "126",
pages = "342",
doi = "10.1007/s00339-020-03524-3"
}
Jakšić, O., Jokić, I., Jakšić, Z., Mladenović, I., Radulović, K.,& Frantlović, M.. (2020). The time response of plasmonic sensors due to binary adsorption: analytical versus numerical modeling. in Applied Physics A
Springer Nature., 126, 342.
https://doi.org/10.1007/s00339-020-03524-3
Jakšić O, Jokić I, Jakšić Z, Mladenović I, Radulović K, Frantlović M. The time response of plasmonic sensors due to binary adsorption: analytical versus numerical modeling. in Applied Physics A. 2020;126:342.
doi:10.1007/s00339-020-03524-3 .
Jakšić, Olga, Jokić, Ivana, Jakšić, Zoran, Mladenović, Ivana, Radulović, Katarina, Frantlović, Miloš, "The time response of plasmonic sensors due to binary adsorption: analytical versus numerical modeling" in Applied Physics A, 126 (2020):342,
https://doi.org/10.1007/s00339-020-03524-3 . .

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