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Quality-by-design and current good practices for the production of test and reference materials for micro- and nano-plastic research
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Researchers outlined best practices for producing standardized reference materials used in micro- and nanoplastic research, covering both top-down fragmentation and bottom-up precipitation methods. They provided examples using common polymers like polyethylene, polypropylene, polystyrene, and PET across different size ranges. The study addresses a critical need in the field, since reliable reference materials are essential for validating the analytical methods used to detect and measure plastic pollution.
Understanding the environmental and human health impacts of micro- and nanoplastic pollutants is currently a high priority, stimulating intensive methodological research work in the areas of sampling, sample preparation and detection as well as intensive monitoring and testing. It is challenging to identify and quantify microplastics in complex organic matrices and concepts for nanoplastic detection are still in their infancy. All analytical techniques employed in studying micro- and nanoplastics require suitable reference materials for validation measurements, with requirements as diverse as the analytical tools used, ranging from different polymer types, size distributions and shapes of the material to the concentrations employed in different experimental set ups (ng to g amounts). The aim of this manuscript is to outline current good practices for small-scale laboratory production and characterization of suitable test and reference materials. The focus is placed on top-down fragmentation methods as well as bottom-up precipitation methods. Examples using polyethylene, polypropylene, polystyrene and polyethylene terephthalate with size distribution classes of mainly 10-1000, 1-10 and < 1 µm particles will be provided. Experiences and suggestions on how to produce well-characterized micro- and nano-plastics for internal research needs will ensure that studies using the materials have robust and informative outcomes.
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