Details
| Original language | English |
|---|---|
| Pages (from-to) | 147-159 |
| Number of pages | 13 |
| Journal | Journal of Computational Electronics |
| Volume | 19 |
| Issue number | 1 |
| Early online date | 13 Nov 2019 |
| Publication status | Published - 1 Mar 2020 |
Abstract
Nanowire field-effect sensors have recently been developed for label-free detection of biomolecules. In this work, we introduce a computational technique based on Bayesian estimation to determine the physical parameters of the sensor and, more importantly, the properties of the analyte molecules. To that end, we first propose a PDE-based model to simulate the device charge transport and electrochemical behavior. Then, the adaptive Metropolis algorithm with delayed rejection is applied to estimate the posterior distribution of unknown parameters, namely molecule charge density, molecule density, doping concentration, and electron and hole mobilities. We determine the device and molecules properties simultaneously, and we also calculate the molecule density as the only parameter after having determined the device parameters. This approach makes it possible not only to determine unknown parameters, but it also shows how well each parameter can be determined by yielding the probability density function (pdf).
Keywords
- Adaptive Metropolis–Hastings algorithm, Markov chain Monte Carlo, Silicon nanowire sensors, Stochastic drift–diffusion–Poisson–Boltzmann system
ASJC Scopus subject areas
- Materials Science(all)
- Electronic, Optical and Magnetic Materials
- Physics and Astronomy(all)
- Atomic and Molecular Physics, and Optics
- Mathematics(all)
- Modelling and Simulation
- Engineering(all)
- Electrical and Electronic Engineering
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In: Journal of Computational Electronics, Vol. 19, No. 1, 01.03.2020, p. 147-159.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Bayesian inversion for nanowire field-effect sensors
AU - Khodadadian, Amirreza
AU - Stadlbauer, Benjamin
AU - Heitzinger, Clemens
N1 - Funding Information: The authors acknowledge support by the FWF (Austrian Science Fund) START project No. Y660 PDE Models for Nanotechnology. The authors also acknowledge the helpful comments by the anonymous reviewers. Funding Information: The authors acknowledge support by the FWF (Austrian Science Fund) START project No. Y660 PDE Models for Nanotechnology . The authors also acknowledge the helpful comments by the anonymous reviewers. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
PY - 2020/3/1
Y1 - 2020/3/1
N2 - Nanowire field-effect sensors have recently been developed for label-free detection of biomolecules. In this work, we introduce a computational technique based on Bayesian estimation to determine the physical parameters of the sensor and, more importantly, the properties of the analyte molecules. To that end, we first propose a PDE-based model to simulate the device charge transport and electrochemical behavior. Then, the adaptive Metropolis algorithm with delayed rejection is applied to estimate the posterior distribution of unknown parameters, namely molecule charge density, molecule density, doping concentration, and electron and hole mobilities. We determine the device and molecules properties simultaneously, and we also calculate the molecule density as the only parameter after having determined the device parameters. This approach makes it possible not only to determine unknown parameters, but it also shows how well each parameter can be determined by yielding the probability density function (pdf).
AB - Nanowire field-effect sensors have recently been developed for label-free detection of biomolecules. In this work, we introduce a computational technique based on Bayesian estimation to determine the physical parameters of the sensor and, more importantly, the properties of the analyte molecules. To that end, we first propose a PDE-based model to simulate the device charge transport and electrochemical behavior. Then, the adaptive Metropolis algorithm with delayed rejection is applied to estimate the posterior distribution of unknown parameters, namely molecule charge density, molecule density, doping concentration, and electron and hole mobilities. We determine the device and molecules properties simultaneously, and we also calculate the molecule density as the only parameter after having determined the device parameters. This approach makes it possible not only to determine unknown parameters, but it also shows how well each parameter can be determined by yielding the probability density function (pdf).
KW - Adaptive Metropolis–Hastings algorithm
KW - Markov chain Monte Carlo
KW - Silicon nanowire sensors
KW - Stochastic drift–diffusion–Poisson–Boltzmann system
UR - http://www.scopus.com/inward/record.url?scp=85075204088&partnerID=8YFLogxK
U2 - 10.1007/s10825-019-01417-0
DO - 10.1007/s10825-019-01417-0
M3 - Article
AN - SCOPUS:85075204088
VL - 19
SP - 147
EP - 159
JO - Journal of Computational Electronics
JF - Journal of Computational Electronics
SN - 1569-8025
IS - 1
ER -