Document Type

Article

Publication Date

2018

College/Unit

Statler College of Engineering and Mining Resources

Department/Program/Center

Lane Department of Computer Science and Electrical Engineering

Abstract

Significant progress has been made on the development of electrolyte-gated graphene field effect transistor (EGGFET) biosensors over the last decade, yet they are still in the stage of proof-of-concept. In this work, we studied the electrolyte matrix effects, including its composition, pH and ionic strength, and demonstrate that variations in electrolyte matrices have a significant impact on the Fermi level of the graphene channel and the sensitivity of the EGGFET biosensors. This is attributed to the polarization-induced interaction between the electrolyte and the graphene at the interface which can lead to considerable modulation of the Fermi level of the graphene channel. As a result, the response of the EGGFET biosensors is susceptible to the matrix effect which might lead to high uncertainty or even false results. Then, an EGGFET immunoassay is presented which aims to allow good regulation of the matrix effect. The multichannel design allows in-situ calibration with negative control, as well as statistical validation of the measurement results. Its performance is demonstrated by the detection of human immunoglobulin G (IgG) from serum. The detection range is estimated to be around 2–50 nM with a coefficient of variation (CV) of less than 20% and the recovery rate for IgG detection is around 85–95%. Compared with traditional immunoassay techniques, the EGGFET immunoassay is label-free and ready to be integrated with microfluidics sensor platforms, suggesting its great prospect for point-of-care applications.

Source Citation

Sun, J.; Liu, Y. Matrix Effect Study and Immunoassay Detection Using Electrolyte-Gated Graphene Biosensor. Micromachines 2018, 9, 142. https://doi.org/10.3390/mi9040142

Comments

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

This article received support from the WVU Libraries' Open Access Author Fund.

micromachines-09-00142-s001.pdf (2336 kB)
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