Enhancing Sensitivity in Multitarget Biomolecule Detection via Engineered Reduced Graphene Oxide Sensors

The selection and immobilization of biorecognition elements (BEs) are critical factors that have attracted significant attention in biosensor development. The compact size of certain BEs, such as aptamers, provides a distinct advantage for field-effect transistor (FET) biosensors by ensuring that binding events occur within the Debye length (a few nanometers). However, the immobilization strategy is a key determinant of the sensor performance. Recent studies indicate that an excessively dense aptamer layer can restrict the necessary conformational changes upon target binding, thereby reducing the sensitivity. In diagnostics, simultaneous detection of multiple biomarkers is essential for the creation of comprehensive diagnostic profiles. This capability enables the early detection and monitoring of complex diseases linked to neurological, metabolic, and oncological pathways. Building on this concept, we have developed a reduced graphene oxide (rGO)-modified extended-gate FET (rGO-EGFET) platform for the multitarget detection of small biomolecules, including serotonin and glucose. To enhance the sensing performance, the surface properties of the rGO were modulated via UV/O3 treatment. This approach effectively controlled the density of the two distinct bioreceptors immobilized on the surface. For serotonin detection, this provided sufficient spacing for conformational flexibility of the aptamer, while for glucose, it modulated the arrangement of the pyrene-based BEs. The obtained sensor array exhibited excellent selectivity toward the target analytes and maintained stable operation across physiologically relevant concentration ranges. The limits of detection were determined to be 48.2 pM for serotonin, 2.4 pM for glucose, and 17 mV/pH, confirming the high sensitivity of the platform for small-biomolecule detection. These results highlight the potential of interface-engineered rGO-EGFETs as versatile and high-performance wearable biosensing platforms, extending the applicability of rGO-based FETs into challenging domains such as neurotransmitter monitoring.