This study reports biosensing using graphene field-effect transistors with the aid of pyrene-tagged DNA aptamers, which exhibit excellent selectivity, affinity, and stability for Escherichia coli (E. coli) detection. The aptamer is employed as the sensing probe due to its advantages such as high stability and high affinity toward small molecules and even whole cells. The change of the carrier density in the probe-modified graphene due to the attachment of E. coli is discussed theoretically for the first time and also verified experimentally. The conformational change of the aptamer due to the binding of E. coli brings the negatively charged E. coli close to the graphene surface, increasing the hole carrier density efficiently in graphene and achieving electrical detection. The binding of negatively charged E. coli induces holes in graphene, which are pumped into the graphene channel from the contact electrodes. The carrier mobility, which correlates the gate voltage to the electrical signal of the APG-FETs, is analyzed and optimized here. The excellent sensing performance such as low detection limit, high sensitivity, outstanding selectivity and stability of the graphene biosensor for E. coli detection paves the way to develop graphene biosensors for bacterial detection.
Foodborne illness is correlated with the existence of infectious pathogens such as bacteria in food and drinking water. Probe-modified graphene field effect transistors (G-FETs) have been shown to be suitable for Escherichia coli (E. coli) detection. Here, the G-FETs for bacterial detection are modeled and simulated with COMSOL Multiphysics to understand the operation of the biosensors. The motion of E. coli cells in electrolyte and the surface charge of graphene induced by E. coli are systematically investigated. The comparison between the simulation and experimental data proves the sensing probe size to be a key parameter affecting the surface charge of graphene induced by bacteria. Finally, the relationship among the change in source-drain current (∆Ids), graphene-bacteria distance and bacterial concentration is established. The shorter graphene-bacteria distance and higher bacterial concentration give rise to better sensing performance (larger ∆Ids) of the G-FETs biosensors. The simulation here could serve as a guideline for the design and optimization of G-FET biosensors for various applications.
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