Mentor: Brice Loose (University of Rhode Island)

Project Location

University of Rhode Island – Bay Campus

Project Description

This project will evaluate the capacity of microfluidics to individuate, enumerate, and visualize microplastics in the environment, using an in-line methodology that can sample continuously from environmental waters to produce a high-resolution census of microplastic particles. Microfluidic separation techniques have the potential to significantly reduce the sampling effort involved in filtering, isolating, and imaging microplastics. Demonstrating the capabilities of this technique in Narragansett Bay can provide a new perspective on the types, concentrations, and variations of microplastic that influence Rhode Island waters coastal waters. If this approach is successful, it can open a broad spectrum of microfluidic manipulation techniques to carry out in-line separation of microplastic particles for on- and off-line analyses. We can feasibly execute this evaluation using the EPSCoR Imaging Flow Cytobot (IFCB) instrument. The IFCB uses laminar flow to organize particles into a linear stream that can be individually imaged and subsequently analyzed. We plan to deploy the EPSCoR IFCB alongside an existing plankton IFCB to capture coincident data sets with and without the fluorescence trigger. Differencing these data streams will reveal the abiotic particles.

At its core, this project will develop a novel data stream of visual imagery. This imagery is essential to quantitation of microplastics as an anthropogenic impact on Narragansett Bay. At the same time, the data stream can help visualize and comprehend a previously unseen component of our aquatic environment. These characteristics address two of the C-AIM core research questions (1) new innovations in sensor deployment that are needed to improve the collection of physical, biogeochemical, and ecological data that are relevant to monitoring and assessing the impacts of anthropogenic stressors on Narragansett Bay. Next, it represents a (2) novel approach to the visualization of complex information, fostered through the collaboration of artists, designers, engineers and scientists to promote broader engagement in and understanding of scientific research, data, and findings. In the same vein this STAC project can serve as a valuable bridging activity between C-AIM and the objectives of the EPSCoR Track-1 renewal proposal, focused on emerging contaminants in coastal ecosystems.

This project involves:

• field work
• lab work
• computational work

Available for SURF Flex?

Yes

Required/preferred skills

Required:

• Note taking and organization skills for working in field and lab environments in order to gather, precise, well-documented, uncontaminated samples and information.
• Familiarity with basic data analysis statistics by spreadsheet or graphing program, including regression analysis, statistical parameter determination, hypothesis testing.
• Enthusiasm, professionalism, and willingness to work in diverse teams.

Preferred:

• Experience programming in Python, Matlab or R.
• Background in image analysis and computer vision by Image-J or OpenCV.
• Analytical and wet chemistry experience.
• Experience using and analyzing data from Raman and Fourier Transform Spectroscopy.

2022