State-Space Modeling for Graphene FET Biosensor Dynamic Response

Ushiba, Shota; Okino, Takashi; Miyakawa, Naruto; Ono, Toshikazu; Shinagawa, Ayumi; Kanai, Yasushi; Inoue, Kōichi; Takahashi, Kazukiyo; Kimura, Masahiko; Matsumoto, Kazuhiko

doi:10.7567/ssdm.2019.d-3-02

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“…We have previously proposed simple state-space models (SSMs) to describe the time-series data of a G-FET biosensor. 13) Herein, we present more sophisticated SSMs and provide detailed discussions. State-space modeling is a framework established to understand stochastic and deterministic dynamical systems, referred to as states, which are observed through a stochastic process.…”

Section: Introductionmentioning

confidence: 99%

State-space modeling for dynamic response of graphene FET biosensors

Ushiba

Okino

Miyakawa

et al. 2020

Jpn. J. Appl. Phys.

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Graphene field effect transistor (G-FET) biosensors exhibit high sensitivity owing to their high electron/hole mobilities and unique 2D nature. However, a baseline drift is observed in their response in aqueous environment, making it difficult to analyze their response against target molecules. Here, we present a computational approach to build state-space models (SSMs) for the time-series data of a G-FET biosensor; the approach helps separate the response against target molecules from the baseline drift. The charge neutral point of the G-FET sensor was continuously measured while sensing target molecules. The obtained time-series data were modeled using the proposed SSMs. The model parameters were estimated through Markov chain Monte Carlo methods. The SSMs were evaluated using the widely-applicable Bayesian information criterion. The SSMs well fitted the time-series data of the G-FET biosensor, and the sensor response to target molecules was extracted from the baseline-drift data.

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Section: Introductionmentioning

confidence: 99%