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The accumulation of micro/nanoplastics (MNPs) in ecosystems poses tremendous environmental risks for terrestrial and aquatic organisms. Designing rapid, field-deployable, and sensitive devices for assessing the potential risks of MNPs pollution is critical. However, current techniques for MNPs detection have limited effectiveness. Here, we design a wireless portable device that allows rapid, sensitive, and on-site detection of MNPs, followed by remote data processing via machine learning algorithms for quantitative fluorescence imaging. We utilized a supramolecular labeling strategy, employing luminescent metal-phenolic networks composed of zirconium ions, tannic acid, and rhodamine B, to efficiently label various sizes of MNPs (e.g., 50 nm-10 μm). Results showed that our device can quantify MNPs as low as 330 microplastics and 3.08 × 10
Ye et al. (Mon,) studied this question.
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