Publication | Closed Access
Development of a Point-of-Care Testing Platform With a Nanogap-Embedded Separated Double-Gate Field Effect Transistor Array and Its Readout System for Detection of Avian Influenza
77
Citations
30
References
2010
Year
NanosensorsMedical ElectronicsEngineeringPoint-of-care TestingPoint-of-care Testing PlatformBiochemical SensorsBiomedical EngineeringBiosensorsMedical InstrumentationBiosensor CartridgeBiosensing SystemsBiomedical DevicesNanosensorReadout SystemDiagnostic DeviceWearable BiosensorsElectrical EngineeringAvian InfluenzaSensor ApplicationsBioinstrumentationBiosensor ArrayBiomedical SensorsBiomedical DiagnosticsBioelectronicsMedicine
Label-free electrical detection of avian influenza (AI) is demonstrated for the development of a point-of-care testing (POCT) platform. For a new POCT platform, a novel field effect transistor (FET)-based biosensor array was fabricated with conventional complementary metal-oxide-semiconductor (CMOS) technology. Nanogap-embedded separated double-gate FETs (nanogap-DGFETs) were realized in a 6 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\,\times\,$</tex> </formula> 6 array as a biosensor cartridge. Moreover, the low-noise readout circuit was designed and fabricated using a 0.35- <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex Notation="TeX">$\mu$</tex></formula> m standard CMOS process. The AI antigen and antibody were bound with the aid of silica-binding proteins (SBP) in the nanogap of the biosensor device. Because the gate dielectric constant was increased by the immobilized biomolecules, the threshold voltage of the nanogap-DGFET was reduced while the drain-to-source current was enhanced. Drain-to-source currents of the nanogap-DGFET array were successfully acquired using the fabricated readout circuitry and measurement setup. This platform is suitable for a simple and effective label-free detection of AI in POCT applications.
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