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An optimized multi-technique based analytical platform for identification, characterization and quantification of nanoplastics in water
Summary
Researchers developed an analytical platform combining flow fractionation, light scattering, and pyrolysis-GC/MS to simultaneously identify polymer type, measure particle size distribution, and quantify nanoplastics in water samples down to 0.01 ppm, filling a key gap in nanoplastic monitoring tools.
Nanoplastics (NPs) have been identified as an emerging concern for the environment and our food chains in recent years. Monitoring the concentration and size of nanoplastics is essential to assess the potential risks that nanoplastic particles may pose. In this study, we presented a multi-technique based analytical platform to identify, characterize and quantify nanoplastics in water samples through a combination of sample pre-concentration, asymmetric flow field-flow fractionation coupled with multi-angle light scattering (AF4-MALS) and pyrolysis-GC/MS (Py-GC/MS). Models for predicting NPs concentration and particle number in unknown samples were established and validated using NPs standards of known size and AF4-MALS response. Py-GC/MS was applied for further identification of polymer type and quantification of mass concentration. Filtration conditions for pre-concentration were optimized to ensure a high recovery rate with minimal effect on original particle size. The addition of 0.05% SDS prior to filtration, using controlled filtration procedures, effectively improved the recovery. Furthermore, this study demonstrates the application of the analytical platform for the characterization and quantification of different nanoparticles (e.g. spiked PMMA and PS NPs) in the size range 60 nm-350 nm with detection limits down to 0.01 ppm in water samples. The established analytical platform can fill an analytical gap by offering a solution for quantifying size-resolved mass concentrations of nanoplastics and providing comprehensive data on size distribution, particle number and mass quantification with high sensitivity for detection.
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