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Article ? AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button. Tier 2 ? Original research — experimental, observational, or case-control study. Direct primary evidence. Detection Methods Environmental Sources Human Health Effects Marine & Wildlife Nanoplastics Sign in to save

Rapid Detection of Micro/Nanoplastics Via Integration of Luminescent Metal Phenolic Networks Labeling and Quantitative Fluorescence Imaging in A Portable Device

2023 9 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 50 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Haoming Yang, Haoxin Ye, Haoxin Ye, Haoxin Ye, Haoxin Ye, Haoxin Ye, Haoxin Ye, Teresa M. Seifried Matthew Kowal, Teresa M. Seifried Haoxin Ye, Teresa M. Seifried Haoxin Ye, Teresa M. Seifried Teresa M. Seifried Haoming Yang, Haoxin Ye, Xinzhe Zheng, Xinzhe Zheng, Teresa M. Seifried David D. Kitts, Haoxin Ye, Haoxin Ye, Teresa M. Seifried Teresa M. Seifried Teresa M. Seifried Teresa M. Seifried Xinzhe Zheng, Xinzhe Zheng, Xinzhe Zheng, Xinzhe Zheng, Tianxi Yang, Tianxi Yang, Haoming Yang, Haoming Yang, Haoming Yang, Matthew Kowal, Matthew Kowal, Matthew Kowal, Matthew Kowal, Edward R. Grant, Edward R. Grant, Edward R. Grant, Tianxi Yang, Tianxi Yang, Edward R. Grant, Edward R. Grant, Teresa M. Seifried Teresa M. Seifried Tianxi Yang, Teresa M. Seifried Edward R. Grant, Edward R. Grant, Gurvendra Pal Singh, David D. Kitts, Tianxi Yang, Gurvendra Pal Singh, Gurvendra Pal Singh, Rickey Y. Yada, Krishna Aayush, Krishna Aayush, Rickey Y. Yada, Rickey Y. Yada, Krishna Aayush, Guanghui Gao, Rickey Y. Yada, Edward R. Grant, Rickey Y. Yada, Guanghui Gao, Guanghui Gao, Edward R. Grant, Edward R. Grant, Edward R. Grant, Edward R. Grant, Tianxi Yang, Tianxi Yang, Tianxi Yang, David D. Kitts, David D. Kitts, Rickey Y. Yada, Rickey Y. Yada, Rickey Y. Yada, Rickey Y. Yada, Rickey Y. Yada, Tianxi Yang, Tianxi Yang, Tianxi Yang, Teresa M. Seifried

Summary

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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