Ye Haoxin, Zheng Xinzhe, Yang Haoming, Kowal Matthew D, Seifried Teresa M, Singh Gurvendra Pal, Aayush Krishna, Gao Guang, Grant Edward, Kitts David, Yada Rickey Y, Yang Tianxi
Food, Nutrition and Health, Faculty of Land and Food Systems, The University of British Columbia, Vancouver, British Columbia V6T1Z4, Canada.
Department of Computer Science, Faculty of Engineering, The University of Hong Kong, Hong Kong 999077, China.
ACS Sens. 2024 Sep 27;9(9):4662-4670. doi: 10.1021/acssensors.4c00957. Epub 2024 Aug 12.
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 nanoplastics in less than 20 min. We demonstrated the applicability of the device to real-world samples through determination of MNPs released from plastic cups after hot water and flow induction and nanoplastics in tap water. Moreover, the device is user-friendly and operative by untrained personnel to conduct data processing on the APP remotely. The analytical platform integrating quantitative imaging, customized data processing, decision tree model, and low-cost analysis ($0.015 per assay) has great potential for high-throughput screening of MNPs in agrifood and environmental systems.
微/纳米塑料(MNPs)在生态系统中的积累对陆地和水生生物构成了巨大的环境风险。设计快速、可现场部署且灵敏的设备来评估MNPs污染的潜在风险至关重要。然而,当前用于MNPs检测的技术效果有限。在此,我们设计了一种无线便携式设备,可实现对MNPs的快速、灵敏且现场检测,随后通过机器学习算法进行远程数据处理以实现定量荧光成像。我们采用了一种超分子标记策略,利用由锆离子、单宁酸和罗丹明B组成的发光金属酚醛网络,有效地标记各种尺寸的MNPs(例如,50纳米至10微米)。结果表明,我们的设备能够在不到20分钟的时间内对低至330个微塑料和3.08×10个纳米塑料进行定量。通过测定塑料杯在热水和流动诱导后释放的MNPs以及自来水中的纳米塑料,我们证明了该设备对实际样品的适用性。此外,该设备用户友好,未经培训的人员也可操作,通过APP进行远程数据处理。这个集成了定量成像、定制数据处理、决策树模型和低成本分析(每次检测0.015美元)的分析平台在农业食品和环境系统中对MNPs进行高通量筛选具有巨大潜力。