Active dual-gated graphene transistors for low-noise, drift-stable, and tunable chemical sensing

Graphene field-effect transistors (GFETs) are among the most promising platforms for ultrasensitive chemical and biological sensing due to their high carrier mobility, large surface area, and low intrinsic noise. However, conventional single-gate GFETs in liquid environments suffer from severe limitations, including signal drift, charge trapping, and insufficient signal amplification. Here, we introduce a dual-gate GFET architecture that integrates a high-κ hafnium dioxide local back gate with an electrolyte top gate, coupled with real-time feedback biasing. This design enables capacitive signal amplification while simultaneously suppressing gate leakage and low-frequency noise. By systematically evaluating seven distinct operational modes, we identify the Differential Mode Fixed configuration as optimal, achieving up to 20× signal gain, >15× lower drift compared with gate-swept methods, and up to 7× higher signal-to-noise ratio across a diverse range of analytes, including neurotransmitters, volatile organic compounds, environmental contaminants, and proteins. We further demonstrate robust multichannel detection using a PCB-integrated GFET sensor array, underscoring the scalability and practicality of the platform for portable, high-throughput sensing. Together, these advances establish a versatile and stable sensing technology capable of real-time, label-free detection of molecular targets under ambient and physiological conditions, with broad applicability in health monitoring, food safety, agriculture, and environmental screening.

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Publication Details

Journal
npj 2D Materials and Applications
Published
2026-02-13
DOI
https://doi.org/10.1038/s41699-026-00674-5
Primary Topic
Graphene research and applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Active dual-gated graphene transistors for low-noise, drift-stable, and tunable chemical sensing

Heshmat Asgharian, Hossein Chenani, Aida Ebrahimi, Vinay Kammarchedu
npj 2D Materials and Applications
Graphene research and applications
article

Active dual-gated graphene transistors for low-noise, drift-stable, and tunable chemical sensing

Heshmat Asgharian, Hossein Chenani, Aida Ebrahimi, Vinay Kammarchedu
article en

Abstract

Graphene field-effect transistors (GFETs) are among the most promising platforms for ultrasensitive chemical and biological sensing due to their high carrier mobility, large surface area, and low intrinsic noise. However, conventional single-gate GFETs in liquid environments suffer from severe limitations, including signal drift, charge trapping, and insufficient signal amplification. Here, we introduce a dual-gate GFET architecture that integrates a high-κ hafnium dioxide local back gate with an electrolyte top gate, coupled with real-time feedback biasing. This design enables capacitive signal amplification while simultaneously suppressing gate leakage and low-frequency noise. By systematically evaluating seven distinct operational modes, we identify the Differential Mode Fixed configuration as optimal, achieving up to 20× signal gain, >15× lower drift compared with gate-swept methods, and up to 7× higher signal-to-noise ratio across a diverse range of analytes, including neurotransmitters, volatile organic compounds, environmental contaminants, and proteins. We further demonstrate robust multichannel detection using a PCB-integrated GFET sensor array, underscoring the scalability and practicality of the platform for portable, high-throughput sensing. Together, these advances establish a versatile and stable sensing technology capable of real-time, label-free detection of molecular targets under ambient and physiological conditions, with broad applicability in health monitoring, food safety, agriculture, and environmental screening.

npj 2D Materials and ApplicationsVol. 10(1)
Pennsylvania State University (US)
National Science Foundation, Division of Materials Research, Division of Electrical, Communications and Cyber Systems
Zero hunger
Openalex Percentile: Top 58%
Graphene research and applications
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