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A method for efficient separation of polystyrene nanoplastics and its application in natural freshwater
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Researchers developed a method using asymmetrical flow field-flow fractionation (AF4) coupled with multiple detectors to efficiently separate and characterize polystyrene nanoplastics by particle size in freshwater environments, demonstrating its applicability for analysing nanoplastic environmental behaviour in natural freshwater samples.
Nanoplastics (NPs) are an emerging contaminant in natural freshwater environments. However, there is a lack of analytical methods for separating and characterizing NPs by particle size, which is essential for analyzing their environmental behavior. Asymmetrical flow field-flow fractionation (AF4) is considered a reliable technique suitable for separating and characterizing the particle size of macromolecules, colloids, and particles. In this work, we report a method for separating and characterizing the size of polystyrene nanoplastics (PS NPs) in freshwater environments using AF4 coupled with MALS and UV-vis detectors. By optimizing the injection volume, mobile phase composition, cross-flow rate, and detector flow rate, we achieved the separation of 50 nm and 100 nm PS NPs within 40 min. The average mass recovery rate reached 88.5%, with relative standard deviations of less than 10% for different indicators in repeated measurements. The R 2 value of the linear regression between concentration and UV peak area exceeded 0.99. We applied this method to natural freshwater media and analyzed the particle sizes of particles added to the freshwater media for 0 and 48 hours using dynamic light scattering (DLS). The results revealed that, despite the appearance of a small number of particle aggregates with sizes close to 250 nm in some freshwater media after 48 hours, the optimized AF4 method still effectively separated the majority of the original unaggregated particles. This effective separation demonstrates the practical feasibility of applying the AF4 method to environmental water samples.
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