High levels of nutrients containing nitrogen and phosphorus can lead to harmful algal blooms in freshwater and seawater. As algal blooms develop, they consume oxygen in the water, which can cause dead zones that prevent other organisms from growing. Continuous monitoring of nutrients can detect conditions that may result in a harmful event. This project will develop a sensor platform to improve the detection of charged nutrients. The project will combine laboratory, computer modeling, and field work. Studies will determine how nutrient species with different chemistries and charges are attracted to the surface of the sensor. These studies will be conducted in laboratory water and in water samples collected from Narragansett Bay. The team will use microscopy and other tools to measure the concentration of the charged nutrients under different experimental conditions. Comparing the performance of the sensor in lab water and in water samples will help determine how the sensors can be used in real world applications. The project will provide students with research assistant and intern positions for training in technical and professional development skills. The project will also develop educational material for the classroom.
The ability to rapidly detect ions in solution with high spatiotemporal sensitivity is critical to many areas from environmental monitoring to industrial wastewaters. This project will exploit surface chemistry to address fundamental limitations associated with reproducibly and selectively detecting anionic nutrient pollutants based on continuous-flow Reporter-Enhanced Surface Enhanced Raman Spectroscopy. This collaborative research project will combine laboratory and computational studies at the University of Rhode Island with field sampling and analysis at Roger Williams University. The dynamic response of charged surface ligands arranged in self-assembled monolayers will be examined in the presence of inorganic anions with different charge valency. Mechanistic insight into the dynamic ligand response will be obtained by modeling ligand conformation and self-assembled monolayer structure. Results will be used guide the selection of additional candidate ligands. Laboratory and computational results will be compared with field data from the Narragansett Bay coastal ecosystem. The project will partner with the Rhode Island Network for Excellence in Science and Technology funded through the NSF EPSCoR Research Infrastructure Improvement Program: EPSCoR Collaborations for Optimizing Research Ecosystems to provide training experiences for Summer Undergraduate Research Fellows. The research will be integrated into coursework at the University of Rhode Island, providing timely, societally important, and use-inspired concepts for student learning.
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.