The time response of plasmonic sensors due to binary adsorption: analytical versus numerical modeling
Authors
Jakšić, Olga
Jokić, Ivana

Jakšić, Zoran

Mladenović, Ivana

Radulović, Katarina

Frantlović, Miloš

Article (Accepted Version)

Springer Nature
Metadata
Show full item recordAbstract
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 algorithmsSource:
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
Collections
Institution/Community
IHTMTY - 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 . .