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Characterization of Nile Red-Stained Microplastics through Fluorescence Spectroscopy

Journal of Marine Science and Engineering 2024 12 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 60 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Suparnamaaya Prasad, Andrew Bennett, Michael Triantafyllou

Summary

This study tested a method for detecting microplastics using a fluorescent dye called Nile Red, which makes plastic particles glow under certain light. The researchers found that different types of plastic produce distinct glow patterns, which could help scientists identify what kind of plastic they are looking at. Better detection tools like this are important for tracking microplastic contamination in the environment and understanding human exposure.

Microplastics (MPs), typically defined as plastic fragments smaller than 5 mm, are pervasive in terrestrial and marine ecosystems. There is a need for rapid, portable, low-cost detection systems to assess health and environmental risks. Fluorescent tagging with Nile Red (NR) has emerged as a popular detection method, but variations in fluorescent emissions based on NR solvent, plastic polymer, excitation wavelength, and additives complicate standardization. In this study, seven plastic samples stained with acetone-based NR were analyzed using a fluorescent spectrometer to identify optimal emission peaks across UV-Vis excitation wavelengths. These findings aid in selecting appropriate excitation wavelengths and optical filters for future detection systems. Additionally, a straightforward polymer identification scheme was validated against field-collected plastic samples, whose material composition was confirmed via Fourier Transform Infrared Spectroscopy. This work contributes towards developing accessible microplastic detection technologies by characterizing the fluorescent properties of NR-stained plastics and enhancing the capability for effective environmental monitoring. Future research will expand the dataset to include diverse plastics with varying additives and weathering, and incorporate computer-vision tools for automated data processing and polymer identification.

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