Optimization of Sensor Morphology and Sensing Performance in a Non-enzymatic Graphene FET Biosensor for Detection of Biomolecules in Complex Analytes

Abstract

Recent advances in ultrasensitive electrical biosensors using graphene nanostructures such as nanowalls and nanopores have increased the surface area-to-volume ratio. These structures provide signals at low biomolecule concentrations that are generally insufficient for vital measurements, especially in complex physiological analytes, making practical deployment difficult. A new, reproducible, and scalable chemical technique for constructing smooth graphene nanogrids enables molar biomolecule detection in field-effect transistor (FET) mode. We examine how pore morphology affects the sensing capability of label-free graphene nanoporous FET biosensors, aiming for sub-femtomolar detection limits with a good signal-to-noise ratio (SNR) in blood or urine serum. Despite problems including drain–source current sensitivity overlap due to high quantities of nonspecific antigens, our improved graphene nanogrid sensor detected hepatitis B (Hep-B) surface antigen in serum at sub-femtomolar levels. In serum containing 3 nM hepatitis C (Hep-C) as a nonspecific antigen, a pore diameter of 30 nm and length of 120 nm had the highest SNR and detected 0.25 fM Hep-B. We used a graphene nanogrid FET biosensor in heterodyne mode (80 kHz to 2 MHz) to quantify Hep-B down to 0.3 fM in blood using a probabilistic neural network (PNN) to reduce Debye screening effects. The performance of the PNN exceeded that of the polynomial fit and static neural network models by limiting quantification errors to 10%. Electrical resistance was linearly related to the Hep-C virus core antigen (HCVcAg) concentration (80–550 pg/mL) in real-time tests. After improvement of functionalization parameters, the SNR increased 70%, detecting 0.20 fM Hep-B virus molecules with 60% sensitivity and 6% standard deviation.