Quantitative Principles for Precise Engineering of Sensitivity in Graphene Electrochemical Sensors.

Abstract

A major difficulty in implementing carbon-based electrode arrays with high device-packing density is to ensure homogeneous and high sensitivities across the array. Overcoming this obstacle requires quantitative microscopic models that can accurately predict electrode sensitivity from its material structure. Such models are currently lacking. Here, it is shown that the sensitivity of graphene electrodes to dopamine and serotonin neurochemicals in fast-scan cyclic voltammetry measurements is strongly linked to point defects, whereas it is unaffected by line defects. Using the physics of point defects in graphene, a microscopic model is introduced that explains how point defects determine sensitivity. The predictions of this model match the empirical observation that sensitivity linearly increases with the density of point defects. This model is used to guide the nanoengineering of graphene structures for optimum sensitivity. This approach achieves reproducible fabrication of miniaturized sensors with extraordinarily higher sensitivity than conventional materials. These results lay the foundation for new integrated electrochemical sensor arrays based on nanoengineered graphene.