[19a-E203-1~12] 12.6 Nanobiotechnology

△ [19a-E203-6] State-Space Modeling for Graphene FET Biosensor Dynamic Response

〇Shota Ushiba¹, Tsuyoshi Okino¹, Naruto Miyakawa¹, Takao Ono², Ayumi Shinagawa¹, Yasushi Kanai², Koichi Inoue², Kohei Takahashi¹, Masahiko Kimura¹, Kazuhiko Matsumoto² (1.Murata Manufacturing, 2.Osaka Univ.)

Keywords:Graphene, Biosensor, model

Graphene field effect transistor (G-FET) biosensors exhibit high sensitivity due to their high electron/hole mobilities. However, G-FET biosensors often undergo baseline drift as a result of their instability under aqueous environments, which makes it difficult to analyze the sensor response against target molecules. Here, we present a computational approach to build state-space models (SSMs) for time-series data of G-FET biosensors, which can separate the response against target molecules from the drift. The drain current was continuously measured, while sensing target molecules such as proteins. The obtained time-series data was modeled by the proposed SSMs. The parameters were estimated by using Markov chain Monte Carlo (MCMC) methods. Our models fit the time-series data of the G-FET biosensors well, and extracted the sensor response to target molecules from the baseline-drift data. This study would enable one to accurately analyze the sensor response.