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Co-staining microplastics with Nile Red and Rose Bengal for improved optical quantification
Summary
A co-staining protocol using both Nile Red and Rose Bengal fluorescent dyes was developed and validated for improved optical detection of microplastics, showing that dual staining outperforms single-dye approaches by reducing false positives and improving quantification accuracy in complex environmental samples.
Accurate assessment of microplastic (MP) contamination in environmental samples is crucial not only for understanding the scope of this growing environmental threat but also for quantifying its magnitude and enabling proper risk assessment. However, current methodologies for MP quantification often suffer from inaccuracies due to the difficulty in distinguishing plastic particles from natural organic matter, also due to incomplete digestion of natural polymers during sample treatment. Moreover, the techniques commonly employed are highly time-consuming, further limiting their routine application. This research presents an innovative solution for optical microscopy evaluation: a sequential co-staining technique employing Nile Red (NR) and Rose Bengal (RB) to identify natural vs. synthetic polymer fragments as well as false positives. Two experiments were implemented staining natural polymers (cellulose, protein, lignin, and chitin) and synthetic polymers (Polyvinyl Chloride (PVC), Polystyrene (PS), Polyethylene Terephthalate (PET), Polypropylene (PP), Nylon (NY), High-Density Polyethylene (HDPE) and Low-Density Polyethylene (LDPE)) with the two dyes. The results showed that co-staining is an effective way of separating natural and synthetic fragments and a significant improvement in the accuracy of visual MP identification. Additionally, co-staining the same filter allows to obtain relevant time saving as well as reducing counting and identification errors, since no sample exchange is needed. Application of this novel technique will allow for more reliable monitoring of MP concentrations in various environmental matrices, leading to better-informed risk assessments and mitigation strategies.
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