loading . . . MagNanoTrap Enrichment Empowers Ultra‐Sensitive Quantification of Mixed Nanoplastic Particles From Environmental Water Samples Fe3O4 nanoparticles coated with the bifunctional peptide LCI-DZ-MBP1, where LCI binds the iron oxide surface and MBP1 acts as a universal plastic-binding peptide, combined with pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS), enable efficient enrichment, precise detection, and quantification of mixed plastics (PS, PE, PP, PET, PMMA, PC, Nylon 6, Nylon 66) from river, lake, sea, and wastewater sources.
ABSTRACT
The detection and quantification of nanoplastic particles (NPs) in environmental waters are crucial for monitoring their fate and assessing health impacts. However, the lack of sensitive and universal detection systems hinders effective regulation, as noted by the EU Commission. Here, we present the MagNanoTrap enrichment platform for NPs capture, which integrates a bifunctional peptide (LCI-DZ-MBP1) with Fe3O4 superparamagnetic nanoparticles (SPIONs). The LCI-peptide binds to SPIONs, while the MBP1-peptide serves as a general binder for common polymers such as polypropylene (PP), polyethylene (PE), polystyrene (PS), and polyethylene terephthalate (PET). MagNanoTrap exhibits a maximum adsorption capacity of 3.95 ± 0.14 g/g for PS-COOH500 nm NPs and, when coupled with pyrolysis–gas chromatography/mass spectrometry (Py-GC/MS), enables reliable compositional analysis for mixed NPs and quantification down to 0.061 µg for PS. The achieved sensitivity ensures that a 1 L water sample is usually sufficient to detect and quantify NPs in environmental water samples. High salt ion concentrations promote hydrophobic interactions, enhancing NPs' binding affinity. The versatility of MagNanoTrap was demonstrated through successful enrichment and quantification of diverse NPs, including PP, PS, PE, PET, poly(methyl methacrylate), polycarbonate, nylon 6, and nylon 66, across seven environmental water matrices from river, lake, marine, and wastewater sources. http://dlvr.it/TSWRhD