Complex Emulsions for Rapid, Real-time, and Multiplexed Sensing Array

Abstract

The advancement in urbanization and industrialization, while propelling human well-being in many ways, has left a trail of environmental and health concerns. A significant increase of chemical toxins, disease biomarkers, and foodborne pathogens in our surroundings, directly threatening both the stability of ecosystems and human health. To effectively address this challenge, we need robust and reliable methods for detecting and monitoring these contaminants. While traditional analytical techniques boast impressive sensitivity and accuracy, their real-time and on-site limitations leave a critical gap in the ability to effectively track and address these threats. Developing novel solutions that bridge this gap is essential if we are to safeguard the environment and our health in the face of this growing environmental issues. This thesis describes our progress on the development of real-time, continuous, and multiplexed sensing platform for environmental contaminants. Specifically, we focus on leveraging dynamic behaviors of the chemical molecules at the interfaces between different liquids. We seek to continuously monitor changes in interfacial tensions to create chemical fingerprints for classification and quantification. To do so, we utilize all-liquid complex emulsions (or complex droplets) as sensing particles, leveraging their unique coupling among their chemical, physical, and optical properties. Herein, Chapter 1 describes the design, dynamic properties, and application of complex droplets as chemical sensors. Chapter 2 provides information on the potential of droplet microfluidics for rapid, inexpensive, and sensitive detection techniques and their applications in environmental monitoring and human health safety. Chapter 3 and Appendix A demonstrate a proof-of-concept model system probing interfaces of complex droplets for generating real-time and continuous sensing data by combining droplets with PDMS-based microfluidics. This work spans across the generation of chemical signals at droplet interfaces to transduce optical sensing signals in real-time. It also includes a model multiplexed sensing system that offers simultaneous detection of multiple events occurring at interfaces of individually designed droplets. Finally, Chapter 4 and Appendix B demonstrate the use of a complex droplet-based microfluidics sensing platform to detect and classify per- and polyfluoroalkyl substances (PFAS) in aqueous samples. This work explains the process of real-time and continuous data generation to understand the interfacial behaviors of PFAS molecules at the liquid-liquid interfaces. It also includes the analysis of recorded real-time data to develop an algorithm to differentiate PFAS classes. Our ultimate goal involves using complex droplets to simultaneously study multiple liquid-liquid interfaces and employing statistical analysis methods to quickly identify unknown chemicals in real-time.