EAGER: Inexpensive and Rapid Detection of Per- and Polyfluoalkyl Substances in Drinking Water Supplies Using Macrocycle-Functionalized Gold Nanoparticles

EAGER:使用大环功能化金纳米粒子廉价且快速地检测饮用水源中的全氟烷基和多氟烷基物质

基本信息

  • 批准号:
    2132026
  • 负责人:
  • 金额:
    $ 25万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2021
  • 资助国家:
    美国
  • 起止时间:
    2021-09-01 至 2024-08-31
  • 项目状态:
    已结题

项目摘要

Per- and polyfluoroalkyl substances (PFAS) consist of over 4,000 synthetic chemicals that are used in the manufacturing of a variety products including non-stick cookware, food wrappers, and fire-fighting foams. PFAS are commonly referred to as "forever chemicals" because they are extremely difficult to break down and become persistent once released into the environment. PFAS have emerged as contaminants of great concern for human and ecosystem health due to their toxicity, mobility, and high tendency to accumulate in environmental media (e.g., soils and water) and living organisms and plants. One of the major routes for human exposure to PFAS is via the ingestion of drinking tap water. However, current analytical assays and tools used to measure PFAS concentrations in water (e.g., sample preconcentration followed by liquid chromatography and mass spectrometry) are expensive, time-consuming, and restricted to specialized laboratories. The goal of this high-risk high-reward EAGER project is to develop a novel analytical tool for the inexpensive and rapid detection and quantification of PFAS in water samples based on a new Surface Enhanced Raman Spectroscopy (SERS) platform. The successful completion this project could lead to the development and validation of a rapid, cost effective and more efficient analytical tool to detect and quantify PFAS in drinking water sources. Further benefits to society will be achieved through student education and training including the mentoring of one doctoral student and two undergraduate students.Chronic exposure to PFAS through the ingestion of drinking water has become a global health concern. The overarching goal of this high-risk high-reward EAGER project is to develop an inexpensive and robust method to quantify PFAS in drinking water. To advance this goal, the Principal Investigators (PIs) propose to leverage and integrate recent advances in plasmonics and supramolecular chemistry to 1) extract and concentrate PFAS molecules from water samples onto macrocycles (MCs) functionalized gold (Au) nanoparticles (NPs) immobilized within a bacterial cellulose (BC) matrix, and 2) use laser illumination to excite the PFAS laden MC functionalized Au NPs to generate hot-spot normalized surface-enhanced Raman spectra. The collected Raman spectra are subsequently analyzed by an optical spectrometer to identify the extracted PFAS molecules and estimate their concentration in the tested water samples. To validate their new analytical method, the PIs propose to investigate PFAS detection and quantification under environmentally relevant conditions including in the presence of dissolved organic matter. The successful completion of this project could lead to the development of a compact and modular device for rapid and onsite PFAS quantification in drinking water supplies. Ultimately, the PIs hope that their new analytic approach could shift the liquid chromatography-tandem mass spectrometry-based paradigm currently used by government and industry to monitor the contamination of drinking water sources by PFAS.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
全氟烷基和多氟烷基物质(PFAS)由4,000多种合成化学品组成,用于制造各种产品,包括不粘炊具,食品包装和消防泡沫。PFAS通常被称为“永久化学品”,因为它们一旦释放到环境中就极难分解并具有持久性。PFAS由于其毒性、移动性和在环境介质中积累的高趋势(例如,土壤和水)以及生物体和植物。 人类接触PFAS的主要途径之一是通过饮用自来水。然而,目前用于测量水中PFAS浓度的分析测定和工具(例如,样品预浓缩,然后进行液相色谱和质谱分析)昂贵、耗时,并且仅限于专门的实验室。这个高风险高回报的EAGER项目的目标是开发一种新的分析工具,用于基于新的表面增强拉曼光谱(Sers)平台对水样中的PFAS进行廉价快速的检测和定量。该项目的成功完成可能会导致开发和验证一种快速,具有成本效益和更有效的分析工具,以检测和量化饮用水源中的PFAS。通过对学生的教育和培训,包括对一名博士生和两名本科生的指导,将进一步造福社会。通过饮用水长期暴露于PFAS已成为全球健康问题。这个高风险高回报的EAGER项目的总体目标是开发一种廉价且可靠的方法来量化饮用水中的PFAS。为了推进这一目标,主要研究者(PI)建议利用和整合等离子体和超分子化学的最新进展,以1)从水样中提取和浓缩PFAS分子到固定在细菌纤维素(BC)基质内的大环(MC)功能化金(Au)纳米颗粒(NP)上,以及2)使用激光照射来激发负载PFAS的MC官能化Au NP以产生热点归一化表面增强拉曼光谱。收集的拉曼光谱随后通过光谱仪进行分析,以识别提取的PFAS分子并估计其在测试水样中的浓度。为了验证他们的新分析方法,PI建议在环境相关条件下(包括存在溶解有机物的情况下)研究PFAS的检测和定量。该项目的成功完成可能会导致开发一种紧凑的模块化设备,用于快速和现场PFAS定量饮用水供应。最终,PI希望他们的新分析方法可以改变目前政府和行业使用的基于液相色谱-串联质谱的范式,以监测PFAS对饮用水源的污染。该奖项反映了NSF的法定使命,并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

项目成果

期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Surface-enhanced Raman spectroscopy for emerging contaminant analysis in drinking water
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Haoran Wei其他文献

Thermal/econmic/environmental considerations in a multi-geneation layout with a heat recovery process; A multi-attitude optimization based on ANN approach
具有热回收过程的多联发电布局中的热/经济/环境考虑因素;
  • DOI:
    10.1016/j.csite.2024.104170
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    6.8
  • 作者:
    Gang Du;Haoran Wei;Pradeep Kumar Singh;A. Dutta;B. S. Abdullaeva;Yasser Fouad;Salem Alkhalaf;Ahmed Alkhayyat;A. Deifalla
  • 通讯作者:
    A. Deifalla
Security Issues and Solutions for Connected and Autonomous Vehicles in a Sustainable City: A Survey
可持续城市中联网和自动驾驶汽车的安全问题和解决方案:调查
  • DOI:
    10.3390/su141912409
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    3.9
  • 作者:
    Zhendong Wang;Haoran Wei;Jianda Wang;Xiaoming Zeng;Yuchao Chang
  • 通讯作者:
    Yuchao Chang
Will alleviating energy poverty enhance social trust in China? An approach based on dual machine learning modeling
缓解能源贫困会增强中国的社会信任吗?一种基于双重机器学习建模的方法
  • DOI:
    10.1016/j.eneco.2025.108560
  • 发表时间:
    2025-06-01
  • 期刊:
  • 影响因子:
    14.200
  • 作者:
    Yulin Liu;Haoran Wei
  • 通讯作者:
    Haoran Wei
Decoding colorectal cancer targeted therapy: a bibliometric journey of the last decade (2015–2024)
  • DOI:
    10.1007/s12672-025-02251-6
  • 发表时间:
    2025-04-01
  • 期刊:
  • 影响因子:
    2.900
  • 作者:
    Linpo Zhou;Xuanwei Huang;Jing Shi;Yebin Yang;Fanhe Dong;Haoran Wei;Chenghao Ji;Yuqiang Shan
  • 通讯作者:
    Yuqiang Shan
Performance evaluation of computational methods for splice-disrupting variants and improving the performance using the machine learning-based framework
使用基于机器学习的框架对剪接破坏变体的计算方法进行性能评估并提高性能
  • DOI:
    10.1093/bib/bbac334
  • 发表时间:
    2022-08
  • 期刊:
  • 影响因子:
    9.5
  • 作者:
    Hao Liu;Jiaqi Dai;Ke Li;Yang Sun;Haoran Wei;Hong Wang;Chunxia Zhao;Dao Wen Wang
  • 通讯作者:
    Dao Wen Wang

Haoran Wei的其他文献

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