Steven Koester
University of Minnesota, USA
Title: Sensor Applications of Graphene Quantum Capacitance Varactors
Biography
Biography: Steven Koester
Abstract
Graphene has tremendous potential to form the basis of a powerful platform for biosensing due to its unique combination of properties, including high surface sensitivity, chemical stability, mechanical strength, and biocompatibility. However, to date, nearly all sensor concepts based upon graphene involve direct measurement of the electrical current in graphene. The need for direct electrical connections can limit the range of applications suitable for these devices, including many biological sensing functions, where wire leads can be cumbersome or impractical. In this talk, I will describe a novel sensor concept that utilizes a unique property of graphene, the quantum capacitance effect. The quantum capacitance effect in graphene allows the formation of a variable capacitor (or varactor) which, when integrated with an inductor, can form a passive LC resonator that can be interrogated through near-field inductive coupling. When the varactor is exposed to an external analyte (chemical, biological, etc.), the surface interaction of the analyte with the graphene shifts the charge concentration in the graphene, thus changing the capacitance and the resonant frequency of oscillation. With appropriate functionalization, this interaction can occur selectively to capture a specific chemical or biological target. We have developed a stable, high-yield fabrication process for graphene varactors utilizing a graphene grown by chemical vapor deposition and demonstrated devices with capacitance tuning ratios as high as 1.6-to-1. The varactor performance can be modeled very accurately utilizing a simple analytical model that takes into account disorder and charge trapping effects. We have demonstrated the operation of these devices for a variety of sensing applications, particularly for detection of glucose and acetone, important analytes for the treatment of diabetes. Finally, we have extensively analyzed the parasitic effect of water on the sensor performance and this work provides important insight into how the sensor robustness can be further improved.