NSF Award
2329826
The broader impact/commercial potential of this Partnerships for Innovation – Research Partnerships (PFI-RP) project is to enhance public safety and the economic resilience of our aquaculture industry in the face of ongoing exposures to disease-causing pathogens. Waterborne pathogens cost billions in annual healthcare costs and aquaculture losses in the United States and globally. This project will accelerate the development and application of an easy-to-use and low-cost technological platform offering excellent portability, sensitivity, and speed to detect pathogens from water sources. The anticipated societal and commercial benefits include early warning signals to local and state agencies about the presence of pathogens in coastal waters, preventing human contact and disease spread, an accessible technology that can minimize the economic impacts of pathogens on aquaculture, low-cost and continuous pathogen surveillance for public safety and ecosystem health, and interdisciplinary learning opportunities for students from underrepresented communities in STEM to enrich their entrepreneurship experience and leadership in a business development setting.<br/><br/>The proposed project advances the application of a novel biosensing-based data analytics platform for aquaculture and water-based environmental monitoring. Regular monitoring for pathogens is critical in safeguarding public and ecosystem health, and in supporting aquaculture sustainability and market growth. However, there is a strong need for low-cost yet sensitive surveillance systems for farmers and environmental agencies for source-tracking and rapid detection of disease-causing pathogens before outbreaks occur. The project will adapt the patented biosensing technology for real-time detection and quantification of aquatic pathogens and forecasting of outbreak risks. The objectives of the research are to evaluate the sensitivity, specificity, and stability of the biosensor when deployed in various environmental settings. This will involve field tests to optimize the performance of the biosensor, and the integration of machine learning methods for interpreting sensor data to maintain accuracy in challenging aquatic environments. These developments will substantially improve our ability to monitor waterborne pathogens in coastal environments and in the aquaculture industry.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
