State-space modeling for dynamic response of graphene FET biosensors

Abstract

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.

Ultrasensitive Detection of SARS-CoV-2 Antibody by Graphene Field-Effect Transistors

“…A small current drift exists in the measurement, which is probably attributed to the influences from environment temperature, ion concentration, pH value, and so forth. 23 To ensure the accuracy of the measurement, the drift must be controlled within an acceptable range. 24−27 Here, the real-time |ΔI DS /I 0 | response upon addition of 500 aM analytes is 1.72% at approximately the 27th min, and the drift is only 0.06% after 3 min (Figures 2d and S11).…”

Ionic strength-sensitive and pH-insensitive interactions between C-reactive protein (CRP) and an anti-CRP antibody

“…Recently, label-free methods using field-effect transistors (FETs) [8], surface plasmon resonance [9], and quartz crystal microbalance [10] have been used to detect CRP. In particular, great progress has been made in designing and fabricating FETs using nanomaterials, including Si nanowires [8,11,12], carbon nanotubes [13], and graphene [14][15][16], leading to a sensitive and rapid analysis with miniaturized and integrated sensor platforms [17,18].…”

Graphene-based field-effect transistor biosensors for the rapid detection and analysis of viruses: A perspective in view of COVID-19

“…Ushiba et al. [54] employed state-space modeling to resolve this issue using time-series data of a GFET biosensor. Their model can be successfully employed for precise analysis of GFET biosensor response under aqueous environment.…”

“…Moreover, GFET biosensors often suffer from the issue of baseline drift in their response within the aqueous environment which makes it complicated to analyze biosensor response against target molecules. Ushiba et al 54 employed state-space modeling to resolve this issue using time-series data of a GFET biosensor. Their model can be successfully employed for precise analysis of GFET biosensor response under aqueous environment.…”