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Magnetic Removal of Micro‐ and Nanoplastics from Water—from 100 nm to 100 µm Debris Size

Small 2023 19 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 55 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Henrik Gaß, Tonya M. Kloos, Anna Höfling, Lukas Müller, Linda Rockmann, Dirk W. Schubert, Marcus Halik

Summary

Researchers demonstrated a magnetic method for removing micro- and nanoplastics from water using iron oxide nanoparticles that attract oppositely charged plastic particles. The technique was effective across a wide size range, from 100 nanometers to 100 micrometers, and worked with multiple plastic types. The study suggests that magnetic removal could help address the gap in current wastewater treatment, which struggles to capture the smallest plastic particles.

Study Type Environmental

Clean water is one of the most important resources of the planet but human-made contamination with diverse pollutants increases continuously. Microplastics (<5 mm diameter) which can have severe impacts on the environment, are present worldwide. Degradation processes lead to nanoplastics (<1 µm), which are potentially even more dangerous due to their increased bioavailability. State-of-the-art wastewater treatment plants show a deficit in effectively eliminating micro- and nanoplastics (MNP) from water, particularly in the case of nanoplastics. In this work, the magnetic removal of three different MNP types across three orders of magnitude in size (100 nm-100 µm) is investigated systematically. Superparamagnetic iron oxide nanoparticles (SPIONs) tend to attract oppositely charged MNPs and form aggregates that can be easily collected by a magnet. It shows that especially the smallest fractions (100-300 nm) can be separated in ordinary high numbers (1013 mg-1 SPION) while the highest mass is removed for MNP between 2.5 and 5 µm. The universal trend for all three types of MNP can be fitted with a derived model, which can make predictions for optimizing SPIONs for specific size ranges in the future.

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