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Rapid Detection of Micro/Nanoplastics Via Integration of Luminescent Metal Phenolic Networks Labeling and Quantitative Fluorescence Imaging in A Portable Device
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Researchers developed a portable wireless device for rapid on-site detection of micro- and nanoplastics using fluorescent labeling and machine learning-powered image analysis. The study demonstrates that this approach enables sensitive and quantitative identification of plastic particles in environmental samples, addressing the need for field-deployable monitoring tools.
The fact that there is an accumulation of micro-and nano-plastics (MNPs) in ecosystems which poses tremendous environmental risks for terrestrial and aquatic organisms is undeniable. Thus, designing improved rapid, field-deployable, and sensitive analytical devices that can assess the potential risks of MNPs pollution is critical. Since current techniques for MNPs detection have limited effectiveness, we sought to design a wireless portable device that will allow rapid, sensitive, and on-site detection of MNPs. Coupling this capacity with remote data processing via machine learning algorithms in a mobile device APP will further enable quantitative fluorescence imaging of MNPs. To achieve this goal, we utilized a developed supramolecular labeling strategy, employing luminescent metal-phenolic networks (L-MPNs) composed of zirconium ions, tannic acid, and rhodamine B, to label a wide range of MNP sizes (i.e.,10 μm, 1 μm, 500 nm, and 50 nm). Results showed that our device can quantify MNPs and detect particle quantities as low as 330 micro-plastic particles and 3.08×106 nano-plastic particles in less than 20 min; while also successfully facilitating quantitative analysis of real-world MNPs samples. The determination of diverse types of MNPs released from commercial plastic cups revealed that the quantity of released plastic particles reached ranges of hundred-million after exposure to boiling water and subsequent 30 min cooling. The device was shown to be user-friendly and operative on a mobile APP by untrained personnel to conduct data processing remotely and effectively. The analytical platform integrating quantitative fluorescence imaging, customized data processing, decision tree model and low-cost analysis ($0.015 per assay) has great potential for high-throughput screening of various types of MNPs in agri-food and environmental systems.
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