Development of Label-free, Non-faradaic Electronic Biosensors using Polymer Bio-composites for Rapid Detection of Proteins
- Author(s): Ogata, Alana F
- Advisor(s): Penner, Reginald M
- et al.
In this thesis the development of label-free, non-faradaic, and rapid electronic biosensors based on conducting polymers for point-of-care applications is presented. Issues with label-free, non-faradaic electronic biosensors are addressed. First, we will address issues of poor sensitivity or low sensor signal that is typically seen when redox labels, which provide signal amplification in electrochemical biosensors, are taken out of a biosensor design. We describe two generations of virus-polymer-based biosensors that use impedance spectroscopy to transduce protein binding events. In chapter 2, a first-generation virus biosensor will show that a simple, monolithic design can be sensitive (limit-of-detection of 100 nM) and rapid (response time < 60s) for protein detection by analyzing the impedance spectrum for an optimal sensing regime. In Chapter 3, the virus-bioresistor will be introduced to establish that large amounts of sensor signal can be produced from a label-free, non-faradaic electronic system by taking advantage of impedance spectroscopy measurements on a chemiresistive channel. Equivalent circuit fitting of the impedance Nyquist plot enables independent measurement of channel resistance versus solutions resistance and provides large signals from direct protein binding to the channel surface. The virus bioresistor improves the first-generation virus-based biosensor with a 7.5 nM limit of detection and a 3 -30 s response time. In Chapter 4, we will address the generality of the virus-bioresistor by applying impedance-transduced chemiresitive measurements to a channel composed of porous carbon nanofibers for detection of glucose. Copolymer nanofibers fabricated by electrospinning are doped with fluoride and functionalized with phenylboronic acid for direct binding to glucose. Impedance measurements are ultimately taken at a single, optimal frequency to provide real-time sensing with ultrafast response times < 8 s and a detection range of 50 µM to 5 mM for glucose. In the entirety of this dissertation, each biosensor shows excellent reproducibility with coefficient-of-variation values < 10%.