Multiplex Nanopore Detection of Structurally Diverse Per- and Polyfluoroalkyl Substances in Surface Water.

IF 16 1区 材料科学 Q1 CHEMISTRY, MULTIDISCIPLINARY
ACS Nano Pub Date : 2025-10-17 DOI:10.1021/acsnano.5c12164
Xiaofeng Lu,Dong Zhong,Qi An,Liting Kang,Na Fan,Junjie Cao,Renjie Li,Qianqian Cao,Yudong Zhou,Xiaoyu Du,Shuanghong Yan,Juan Li,Xiaolei Qu,Yuqin Wang
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引用次数: 0

Abstract

Perfluoroalkyl and polyfluoroalkyl substances (PFASs) make up a large class of emerging chemical pollutants that have caused extensive contamination of global water sources. As the toxicity of PFAS becomes increasingly recognized, there is a growing demand for low-cost and rapid sensors capable of screening water samples for multiple PFAS species. However, most electrochemical and optical sensors can detect only one or two PFASs, despite the high structural diversity of these compounds in aquatic environments. Here, we report a single-molecule nanopore sensor that enables simultaneous detection of nine PFASs in a single measurement. By incorporating a β-cyclodextrin (β-CD) adapter into a mutant α-hemolysin (α-HL) nanopore, translocating PFAS molecules produce distinct current blockades that allow clear discrimination based on carbon chain lengths, hydrogen substitutions, and terminal functional groups. With assistance from a machine learning classifier, an overall identification accuracy of 95.83% is achieved. This strategy allows direct, label-free, and rapid discrimination of multiple PFASs in surface water samples at environmentally relevant concentrations as low as the microgram per liter level, without chemical labeling, separation, or enrichment. The successful demonstration of nanopore sensing in complex real-world matrices highlights its strong potential for practical, field-deployable environmental analysis.
地表水中结构多样的全氟烷基和多氟烷基物质的多重纳米孔检测。
全氟烷基和多氟烷基物质构成了一大类新出现的化学污染物,对全球水源造成了广泛污染。随着人们越来越认识到PFAS的毒性,对能够筛选多种PFAS物种的水样的低成本和快速传感器的需求日益增长。然而,大多数电化学和光学传感器只能检测到一种或两种PFASs,尽管这些化合物在水生环境中具有很高的结构多样性。在这里,我们报告了一种单分子纳米孔传感器,可以在一次测量中同时检测9种全氟化合物。通过将β-环糊精(β-CD)转接器整合到突变体α-溶血素(α-HL)纳米孔中,易位的PFAS分子产生了不同的电流阻断,可以根据碳链长度、氢取代和末端官能团进行明确的区分。在机器学习分类器的帮助下,总体识别准确率达到95.83%。该策略可以直接、无标签、快速地识别地表水样品中与环境相关的浓度低至微克/升水平的多种全氟磺酸,而无需化学标记、分离或富集。纳米孔传感在复杂现实世界矩阵中的成功演示,突出了其在实际、可现场部署的环境分析方面的强大潜力。
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来源期刊
ACS Nano
ACS Nano 工程技术-材料科学:综合
CiteScore
26.00
自引率
4.10%
发文量
1627
审稿时长
1.7 months
期刊介绍: ACS Nano, published monthly, serves as an international forum for comprehensive articles on nanoscience and nanotechnology research at the intersections of chemistry, biology, materials science, physics, and engineering. The journal fosters communication among scientists in these communities, facilitating collaboration, new research opportunities, and advancements through discoveries. ACS Nano covers synthesis, assembly, characterization, theory, and simulation of nanostructures, nanobiotechnology, nanofabrication, methods and tools for nanoscience and nanotechnology, and self- and directed-assembly. Alongside original research articles, it offers thorough reviews, perspectives on cutting-edge research, and discussions envisioning the future of nanoscience and nanotechnology.
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