Impact of Self-Assembled Monolayer Design and Electrochemical Factors on Impedance-Based Biosensing

Abstract

Real-time sensing of proteins, especially in wearable devices, remains a substantial challenge due to the need to convert a binding event into a measurable signal that is compatible with the chosen analytical instrumentation. Impedance spectroscopy enables real-time detection via either measuring electrostatic interactions or electron transfer reactions while simultaneously being amenable to miniaturization for integration into wearable form-factors. To create a more robust methodology for optimizing impedance-based sensors, additional fundamental studies exploring components influencing the design and implementation of these sensors are needed. This investigation addresses a sub-set of these issues by combining optical and electrochemical characterization to validate impedance-based sensor performance as a function of (1) biorecognition element density, (2) self-assembled monolayer chain length, (3) self-assembled monolayer charge density, (4) the electrochemical sensing mechanism and (5) the redox reporter selection. Using a pre-existing lysozyme aptamer and lysozyme analyte combination, we demonstrate a number of design criteria to advance the state-of-the-art in protein sensing. For this model system we demonstrated the following: First, denser self-assembled monolayers yielded substantially improved sensing results. Second, self-assembled monolayer composition, including both thickness and charge density, changed the observed peak position and peak current. Third, single frequency measurements, while less informative, can be optimized to replace multi-frequency measurements and in some cases (such as that with zwitterionic self-assembled monolayers) are preferred. Finally, various redox reporters traditionally not used in impedance sensing should be further explored. Collectively, these results can help limit bottlenecks associated with device development, enabling realization of next-generation impedance-based biosensing with customize sensor design for the specific application.