Wearable electronics with real-time biosensing capabilities are very important for future applications in monitoring and augmenting human health and performance. Graphene-based potentiometric sensing offers a route for developing wearable sensors that can selectively sense biomarkers in biofluids such as sweat and saliva. This manuscript studies the sensitivity of potentiometric sensors made with graphene-based electrolyte-gated field-effect transistors (GFETs). Selectivity in the sensor toward a nanoscale biomarker, neuropeptide Y (NPY), was achieved by functionalizing graphene with a peptide-based biorecognition element. The sensors were then characterized extensively by varying concentrations of NPY in a complex medium containing artificial sweat with varying ionic concentrations and pH. This medium, therefore, emulated the response of the sensor to biomarkers in a physiologically relevant condition approaching a real-world scenario. Analysis using Gouy−Chapman−Stern theory for the liquid−solid interface at nanoscale highlighted important features of potentiometric sensing such as log-linear response and charge screening effects in GFET sensors.

Transmission electron microscopy (TEM) is being pushed to new capabilities which enable studies on systems that were previously out of reach. Among recent innovations, TEM through liquid cells (LC-TEM) enables in operando observation of biological phenomena. This work applies LC-TEM to the study of biological components as they interact on an abiotic surface. Specifically, analytes or target molecules like neuropeptide Y (NPY) are observed in operando on functional graphene field-effect transistor (GFET) biosensors. Biological recognition elements (BREs) identified using biopanning with affinity to NPY are used to functionalize graphene to obtain selectivity. On working devices capable of achieving picomolar responsivity to neuropeptide Y, LC-TEM reveals translational motion, stochastic positional fluctuations due to constrained Brownian motion, and rotational dynamics of captured analyte. Coupling these observations with the electrical responses of the GFET biosensors in response to analyte capture and/or release will potentially enable new insights leading to more advanced and capable biosensor designs.

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