Collaborative Research: Environmental Sensing of Per and Polyfluoroalkyl Substances in Water Utilizing a Microelectrode Sensor Array Platform and Machine Learning Enabled Detection

合作研究：利用微电极传感器阵列平台和机器学习检测对水中的全氟烷基和多氟烷基物质进行环境传感

• 批准号: 2149235
• 负责人: Brian Chaplin
• 金额: $ 46.52万
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2149235&HistoricalAwards=false
• 关键词: Collaborative; Research; Environmental; Sensing; Per

Per- and polyfluoroalkyl substances (PFAS) are a group of “forever chemicals” that are used in numerous consumer and industrial products including non-stick cookware, paints, clothes, cleaning products, food packaging, and firefighting foams. These products are either released to the environment or disposed of in landfills and, therefore, have the potential to contaminate natural waters and drinking water sources. Medical studies suggest that exposure to very low levels of PFAS could result in long-term developmental disabilities in infants, increased infertility, and risk of cancer. As such, PFAS contamination is an important environmental problem, and low-cost methods for rapid and reliable monitoring are necessary. Current PFAS detection methods require expensive equipment and specialized training to maintain the complicated instrumentation. The overall objective of this project is to create a low-cost PFAS sensing method to monitor PFAS contamination in water. This objective will be accomplished by developing a microelectrode sensor array coupled with machine learning algorithms to detect a mixture of PFAS in diverse water sources. The successful completion of this project will benefit society through the development of a low-cost method to monitor PFAS. Additional benefits to society will be achieved through student education and outreach including the mentoring of two graduate students at the University of Illinois at Chicago and an undergraduate student at Purdue University.Low-cost methods for rapid and reliable monitoring of per- and polyfluoroalkyl substances (PFAS) are greatly needed. Current PFAS analysis relies on chromatographic methods coupled to expensive and bulky mass spectrometric detectors. While these methods are useful for accurate low-level quantification of PFAS, they are not mobile, and they require specialized training to maintain the complicated instrumentation. The overarching objective of this project is to create a bottom-up framework for the development of mobile, low-cost PFAS sensing platforms that can be used in-situ and at the point-of-use to monitor PFAS contamination in water. The proposed framework will be demonstrated through the development of a functionalized microelectrode sensor array (MESA) platform, coupled with machine learning algorithms, for the detection and quantification of a mixture of PFAS with a range of physical properties in diverse water matrices. The specific research objectives of this project are to: (1) characterize the fundamental adsorption/desorption mechanisms of PFAS on sorbent materials using an electrochemical quartz crystal microbalance experimental setup; (2) utilize computational density functional theory calculations to reveal the specific surface interactions that control PFAS adsorption/desorption on different sorbent materials; (3) integrate the experimental-computational results to guide the selection of selective, reversible adsorbents for various PFAS; and (4) fabricate and test a machine-learning enabled MESA platform for PFAS detection. The successful completion of this project has potential for transformative impact through the development of a sensor for the selective detection of individual compounds within a PFAS mixture with detection limits in the low ng/L concentration range and reliable performance in varying source water matrices. Further benefits to society will be accomplished through an annual summer research experience for undergraduates and by creating a four-week workshop to introduce machine-learning concepts to high school students.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.

每种和多氟烷基物质（PFA）是一组“永远的化学物质”，用于许多消费者和工业产品，包括非粘炊具，油漆，衣服，清洁产品，食品包装和消防泡沫。这些产品要么被释放到环境中，要么在垃圾填埋场中被释放，因此有可能污染天然水和饮用水源。医学研究表明，暴露于极低水平的PFA可能会导致婴儿长期发育障碍，不孕症增加和癌症风险。因此，PFAS污染是一个重要的环境问题，需要进行快速和可靠监测的低成本方法。当前的PFA检测方法需要昂贵的设备和专门的培训来维护复杂的仪器。该项目的总体目的是创建一种低成本的PFAS感应方法来监测水中的PFA污染。该目标将通过开发一个微电极传感器阵列以及机器学习算法来检测潜水员水源中PFA的混合物来实现。该项目的成功完成将通过开发低成本监测PFA的方法来使社会受益。将通过学生的教育和外展活动获得更多的好处，包括在伊利诺伊大学芝加哥大学的两名研究生和普渡大学的本科生进行心理。当前的PFAS分析依赖于色谱法与昂贵且笨重的质谱检测器结合。尽管这些方法可用于准确的PFA低级量化，但它们不是移动的，并且需要专门的培训来维护复杂的仪器。该项目的总体目的是为开发移动，低成本PFAS传感器平台的开发创建一个自下而上的框架，该平台可用于原位和使用时，以监视水中的PFAS污染。提出的框架将通过开发功能化的微电极传感器阵列（MESA）平台，再加上机器学习算法，以检测和定量PFA的混合物与潜水员水材料中的物理特性的混合物。该项目的具体研究目标是：（1）使用电化学石英晶体微量平衡实验设置来表征PFA在吸附材料上的基本吸附/解吸机制； （2）利用计算密度功能理论计算来揭示控制PFA的吸附/解吸不同吸附剂材料的特定表面相互作用； （3）整合了实验计算结果，以指导针对各种PFA的选择性，可逆的吸附剂的选择； （4）制造和测试启用机器学习的台面平台以进行PFAS检测。该项目的成功完成具有通过开发传感器的开发，可以选择性检测PFAS混合物内的单个化合物，该化合物在低Ng/L浓度范围内的检测极限和在不同源水产品中的可靠性能。将通过针对本科生的年度夏季研究经验和创建为期四周的研讨会来向高中生介绍机器学习概念的进一步好处。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响来评估来获得珍贵的支持。