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Nanoplastic pollution is an emerging environmental concern, but current analytical approaches are facing limitations in this size range. However, the coupling of nanoparticle separation with chemical characterization bears potential to close this gap. Here, we realize the hyphenation of particle separation/characterization (field-flow fractionation (FFF), UV, and multiangle light scattering) with subsequent chemical identification by online Raman microspectroscopy (RM). The problem of low Raman scattering was overcome by trapping particles with 2D optical tweezers. This setup enabled RM to identify particles of different materials (polymers and inorganic) in the size range from 200 nm to 5 μm, with concentrations in the order of 1 mg/L (109 particles L–1). The hyphenation was realized for asymmetric flow FFF and centrifugal FFF, which separate particles on the basis of different properties. This technique shows potential for application in nanoplastic analysis, as well as many other fields of nanomaterial characterization.

In the environment the weathering of plastic debris is one of the main sources of secondary microplastic (MP). It is distinct from primary MP, as it is not intentionally engineered, and presents a highly heterogeneous analyte composed of plastic fragments in the size range of 1 µm−1 mm. To detect secondary MP, methods must be developed with appropriate reference materials. These should share the characteristics of environmental MP which are a broad size range, multitude of shapes (fragments, spheres, films, fibers), suspensibility in water, and modified particle surfaces through aging (additional OH, C=O, and COOH). To produce such a material, we bring forward a rapid sonication-based fragmentation method for polystyrene (PS), polyethylene terephthalate (PET), and polylactic acid (PLA), which yields up to 10 5 /15 mL dispersible, high purity MP particles in aqueous media. To satisfy the claim of a reference material, the key properties-composition and size distribution to ensure the homogeneity of the samples, as well as shape, suspensibility, and aging -were analyzed in replicates (N = 3) to ensure a robust production procedure. The procedure yields fragments in the range of 100 nm−1 mm (<20 µm, 54.5 ± 11.3% of all particles). Fragments in the size range 10 µm−1 mm were quantitatively characterized via Raman microspectroscopy (particles = 500-1,000) and reflectance micro Fourier transform infrared analysis (particles = 10). Smaller particles 100 nm−20 µm were qualitatively characterized by scanning electron microcopy (SEM). The optical microscopy and SEM analysis showed that fragments are the predominant shape for all polymers, but fibers are also present. Furthermore, the suspensibility and sedimentation in pure MilliQ water was investigated using ultraviolet-visible spectroscopy and revealed that the produced fragments sediment according to their density and that the attachment to glass is avoided. Finally, a comparison of the infrared spectra from the fragments produced through sonication and naturally aged MP shows the addition of polar groups to the surface of the particles in the OH, C=O, and COOH region, making these particles suitable reference materials for secondary MP.

Micro- and nanoplastic contamination is becoming a growing concern for environmental protection and food safety. Therefore, analytical techniques need to produce reliable quantification to ensure proper risk assessment. Raman microspectroscopy (RM) offers identification of single particles, but to ensure that the results are reliable, a certain number of particles has to be analyzed. For larger MP, all particles on the Raman filter can be detected, errors can be quantified, and the minimal sample size can be calculated easily by random sampling. In contrast, very small particles might not all be detected, demanding a window-based analysis of the filter. A bootstrap method is presented to provide an error quantification with confidence intervals from the available window data. In this context, different window selection schemes are evaluated and there is a clear recommendation to employ random (rather than systematically placed) window locations with many small rather than few larger windows. Ultimately, these results are united in a proposed RM measurement algorithm that computes confidence intervals on-the-fly during the analysis and, by checking whether given precision requirements are already met, automatically stops if an appropriate number of particles are identified, thus improving efficiency.

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