Date of Award
5-15-2024
Degree Name
Doctor of Philosophy (PhD)
Degree Type
Dissertation
Abstract
Optical biosensors are frontrunners for rapid and real-time detection of analytes, particularly for low concentrations. Among them, whispering gallery mode (WGM) resonators have recently attracted a growing focus due to their robust optomechanical and optofluidic features and high sensitivity, measuring down to single binding events in small volumes. With their unique advantages and their compatibility with different sensing modalities, these sensors have the potential to become major game changers for biomedical and environmental monitoring, among many other relevant target applications. In this dissertation we focus on one geometry of whispering gallery mode resonators, the microbubble resonator. This platform is unique, among others, because of its superior ability to integrate fluidic handling and optical sensing. This geometry has inline silica capillaries which are perfectly suited to deliver analytes, in liquid of gas phase, to the sensing area. This dissertation can be broken into two main parts; optimization and applications of the whispering gallery mode microbubble resonator. We begin by optimizing the fabrication procedure by a standardization of the fabrication parameters, leading to higher fabrication success rates and lower cost. Next, we show work into polymer packaging of the resonator platform to protect the device from contamination and outside perturbations – showing applications in displacement sensing as well. The last part of our optimization work gets around the use of expensive tunable diode laser sources typically used in whispering gallery mode resonators and replaces it with a single wavelength source without sacrificing the platforms sensing capabilities. The second part of this dissertation will show two novel applications of the whispering gallery mode microbubble platform. First, we imbue the microbubble resonator with sensitivity to changes in pH, a parameter the device is not inherently sensitive to, through the use of “smart” hydrogels. To isolate the whispering gallery mode from sensing refractive index instead of pH, we employ a “thick-walled” microbubble resonator to protect and insulate the mode from outside influences. Lastly, we show the microbubble resonator coupled to machine learning algorithms can be used to not only detect, but also classify nanoparticles and cells based on their photoacoustic spectroscopy signals.
Language
English (en)
Chair
Lan Yang