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Preliminary Results From Detection of Microplastics in Liquid Samples Using Flow Cytometry
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Researchers developed a novel flow cytometry approach for in-situ detection and quantification of microplastics in liquid samples using fluorescent staining, testing nine polymer types under controlled laboratory conditions. The method offers a high-throughput alternative to traditional time-consuming microplastic detection protocols that risk sample contamination.
Microplastics are globally recognized as contaminants in freshwater and marine aquatic systems. To date there is no universally accepted protocol for isolation and quantification of microplastics from aqueous media. Various methodologies exist, many of which are time consuming and have the potential to introduce contaminants into samples, thereby obscuring characterization of the environmental microplastic load. Here we present a novel approach for in-situ detection of microplastics, based on their fluorescent staining followed by high throughput analysis and quantification using Flow Cytometry. Using controlled laboratory settings nine polymer types (Polystyrene – PS; Polyethylene – PE; Polyethylene terephthalate – PET/PETE; High density polyethylene – HDPE; Low density polyethylene – LDPE; Polyvinyl chloride – PVC; Polypropylene –PP; Nylon – PA; and Polycarbonate - PC) were tested for identification and quantification in freshwater. All nine plastic types were stained with 10 µg/mL Nile Red in 10% dimethyl sulfoxide with a 10 minutes incubation time. The lowest spatial detectable limit for plastic particles was 200 nm. Out of the nine polymer types chosen for the study PS, PE, PET, and PC were well identified; however, results for other plastic types (PVC, PP, PA, LDPE, and HDPE) were masked to certain extent by Nile Red aggregation and precipitation. The methodology presented here permits identification of a range of particle sizes and types. It represents a significant step in the quantification of microplastics by replacing visual data interpretation with a sensitive and automated method.
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