Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 4785 |
| Seitenumfang | 18 |
| Fachzeitschrift | Sensors |
| Jahrgang | 22 |
| Ausgabenummer | 13 |
| Frühes Online-Datum | 24 Juni 2022 |
| Publikationsstatus | Veröffentlicht - 1 Juli 2022 |
Abstract
Silicon nanowire field-effect transistors are promising devices used to detect minute amounts of different biological species. We introduce the theoretical and computational aspects of forward and backward modeling of biosensitive sensors. Firstly, we introduce a forward system of partial differential equations to model the electrical behavior, and secondly, a backward Bayesian Markov-chain Monte-Carlo method is used to identify the unknown parameters such as the concentration of target molecules. Furthermore, we introduce a machine learning algorithm according to multilayer feed-forward neural networks. The trained model makes it possible to predict the sensor behavior based on the given parameters.
ASJC Scopus Sachgebiete
- Chemie (insg.)
- Analytische Chemie
- Informatik (insg.)
- Information systems
- Biochemie, Genetik und Molekularbiologie (insg.)
- Biochemie
- Physik und Astronomie (insg.)
- Atom- und Molekularphysik sowie Optik
- Physik und Astronomie (insg.)
- Instrumentierung
- Ingenieurwesen (insg.)
- Elektrotechnik und Elektronik
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in: Sensors, Jahrgang 22, Nr. 13, 4785, 01.07.2022.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Rational Design of Field-Effect Sensors Using Partial Differential Equations, Bayesian Inversion, and Artificial Neural Networks
AU - Khodadadian, Amirreza
AU - Parvizi, Maryam
AU - Teshehlab, Mohammad
AU - Heitzinger, Clemens
N1 - Funding Information: C. Heitzinger and A. Khodadadian acknowledge support by FWF START project no. Y660 PDE Models for Nanotechnology. M. Parvizi acknowledges the financial support of the Alexander von Humbold Foundation project named H−matrix approximability of the inverses for FEM, BEM and FEM–BEM coupling of the electromagnetic problems. She is affiliated to the Cluster of Excellence PhoenixD (EXC 2122, Project ID 390833453).
PY - 2022/7/1
Y1 - 2022/7/1
N2 - Silicon nanowire field-effect transistors are promising devices used to detect minute amounts of different biological species. We introduce the theoretical and computational aspects of forward and backward modeling of biosensitive sensors. Firstly, we introduce a forward system of partial differential equations to model the electrical behavior, and secondly, a backward Bayesian Markov-chain Monte-Carlo method is used to identify the unknown parameters such as the concentration of target molecules. Furthermore, we introduce a machine learning algorithm according to multilayer feed-forward neural networks. The trained model makes it possible to predict the sensor behavior based on the given parameters.
AB - Silicon nanowire field-effect transistors are promising devices used to detect minute amounts of different biological species. We introduce the theoretical and computational aspects of forward and backward modeling of biosensitive sensors. Firstly, we introduce a forward system of partial differential equations to model the electrical behavior, and secondly, a backward Bayesian Markov-chain Monte-Carlo method is used to identify the unknown parameters such as the concentration of target molecules. Furthermore, we introduce a machine learning algorithm according to multilayer feed-forward neural networks. The trained model makes it possible to predict the sensor behavior based on the given parameters.
KW - Bayesian inversion
KW - biosensors
KW - charge transport
KW - field-effect sensors
KW - inverse modeling
KW - neural networks
UR - http://www.scopus.com/inward/record.url?scp=85132700506&partnerID=8YFLogxK
U2 - 10.3390/s22134785
DO - 10.3390/s22134785
M3 - Article
C2 - 35808281
AN - SCOPUS:85132700506
VL - 22
JO - Sensors
JF - Sensors
SN - 1424-8220
IS - 13
M1 - 4785
ER -