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An analytical strategy for challenging members of the microplastic family: Particles from anti-corrosion coatings
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Researchers developed an analytical strategy for identifying and quantifying particles from anti-corrosion coatings, an often-overlooked fraction of microplastic pollution. The study applied multiple identification techniques to these challenging particles, improving the ability to accurately detect coating-derived microplastics in environmental samples.
Potentially hazardous particles from paints and functional coatings are an overlooked fraction of microplastic (MP) pollution since their accurate identification and quantification in environmental samples remains difficult. We have applied the most relevant techniques from the field of microplastic analysis for their suitability to chemically characterize anti-corrosion coatings containing a variety of polymer binders (LDIR, Raman and FTIR spectroscopy, Py-GC/MS) and inorganic additives (ICP-MS/MS). We present the basis of a possible toolbox to study the release and fate of coating particles in the (marine) environment. Our results indicate that, due to material properties, spectroscopic methods alone appear to be unsuitable for quantification of coating/paint particles and underestimate their environmental abundance. ICP-MS/MS and an optimized Py-GC/MS approach in combination with multivariate statistics enables a straightforward comparison of the multi-elemental and organic additive fingerprints of paint particles. The approach can improve the identification of unknown particles in environmental samples by an assignment to different typically used coating types. In future, this approach may facilitate allocation of emission sources of different environmental paint/coating particles. Indeed, future work will be required to tackle various remaining analytical challenges, such as optimized particle extraction/separation of environmental coating particles.
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Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.