UC Irvine

Antibiotic resistance has emerged as a critical global health threat in the 21st century, leading to untreatable infectious diseases and significantly increasing human morbidity and mortality. Rapid, portable, and user-friendly detection of antibiotics, microbial pathogens, and antibiotic-resistant organisms in aquatic environment plays a key role in controlling the spread of diseases and combating the emergence of antibiotic resistance. Unfortunately, current environmental monitoring practices are tedious, expensive and slow. These limitations impede timely detection and response to emerging threats, allowing the spread of diseases and antibiotic-resistant pathogens to human populations. This dissertation research aims to develop environmental monitoring strategies that streamline the current monitoring timeline and make it accessible to a wider range of users. This effort will contribute to more frequent and efficient monitoring of antibiotics, microbial pathogens, and antibiotic-resistant organisms in water matrices. Through this research, I first investigated the application of Surface Enhanced Raman Scattering (SERS) for label-free monitoring of antibiotics in wastewater. By utilizing carefully designed SERS substrates and artificial intelligence (AI) algorithms, I successfully quantified the target antibiotics even in the presence of other organic and inorganic molecules in wastewater. Next, I developed a portable and semi-automatic virus detection centrifugal microfluidic disc (CD) that integrates sample concentration, purification, amplification, and quantification steps for environmental water monitoring. The on-CD assay is completed in less than 1.5 hours, and its performance is comparable to that of the benchtop in-tube assay. Lastly, building upon the foundation of the virus detection CD system, I further designed a microfluidic CD that integrates the phenotypic bacterial culture with genotypic pathogen identification for monitoring of antibiotic resistant bacteria. This CD system successfully detected indigenous antibiotic-resistant bacteria in raw wastewater in under 3 hours. This dissertation serves as an important foundation for developing more advanced, field-applicable techniques and methodologies in environmental monitoring.