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In-house validation of AF4-MALS-UV for polystyrene nanoplastic analysis

Analytical and Bioanalytical Chemistry 2021 27 citations

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Researchers validated an asymmetric flow field-flow fractionation method coupled with multi-angle light scattering and UV detection for simultaneously sizing and quantifying polystyrene nanoplastics from 20 to 200 nm, achieving greater than 90% recovery and detection limits of 15–33 µg/mL across a polydisperse nanoplastic mixture.

Polymers

The suitability of asymmetric flow field-flow fractionation (AF4) coupled on-line to multi-angle light scattering (MALS) and UV diode array (UV-DAD) detectors was tested to simultaneously detect polystyrene nanoplastics (PS-NPs) and collect information about their size. A mixture of four sizes of PS-NPs at 20 nm, 60 nm, 100 nm and 200 nm was prepared by dilution with ultrapure deionized water and gentle mixing and was used as test sample for a polydisperse nanoplastic system. The AF4 method separated each single size of PS-NP mixture in a total time of 48 min by using 0.2% SDS as carrier solution. Then, the PS-NPs were sized and detected by following their MALS (90° scattering angle) and UV (215 nm) signals. Quality control (QC) performances as linearity, between-day repeatability, resolution factor, trueness/recovery, limit of detection (LoD) and selectivity were calculated, according to the ISO/TS 21362:2018. Method uncertainty was calculated following the ISO/TS 21748:2002 by summing between-day repeatability and trueness or recovery uncertainties. In-house validation results demonstrated good peak resolution and selectivity, R linearity of 0.998-0.999 in the range 50-1000 μg/mL, between-day repeatability of ca. 10%, trueness/recovery above 90% and LoD between 15 μg/mL (20 nm) and 33 μg/mL (200 nm). Expanded uncertainty was 16.1-17.9% on PS-NP size between 60 and 200 nm and 10.4-14.7% on PS-NP concentration between 100 and 1000 μg/mL. Compared to traditional single-technique analysis, this hyphenated method offers great promise for separating and analysing diverse populations of PS-NPs present in real matrices, which is critical for health and risk assessment studies and any regulatory action.

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