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

全氟和多氟烷基物质(PFAS)是一组“永久化学品”，广泛用于各种消费品和工业产品，包括不粘锅、油漆、衣服、清洁产品、食品包装和灭火泡沫。这些产品要么被释放到环境中，要么被丢弃在垃圾填埋场，因此有可能污染自然水和饮用水水源。医学研究表明，暴露于极低水平的全氟辛烷磺酸可能会导致婴儿长期发育障碍，增加不孕症，并有患癌症的风险。因此，全氟辛烷磺酸污染是一个重要的环境问题，有必要采用低成本的方法进行快速可靠的监测。目前的全氟辛烷磺酸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。该项目的总体目标是创建一种低成本的全氟辛烷磺酸传感方法来监测水中的全氟辛烷磺酸污染。这一目标将通过开发与机器学习算法相结合的微电极传感器阵列来实现，以检测不同水源中的全氟辛烷磺酸混合物。该项目的成功完成将通过开发一种监测全氟辛烷磺酸的低成本方法而造福社会。还将通过学生教育和外展活动为社会带来更多好处，包括对伊利诺伊大学芝加哥分校的两名研究生和普渡大学的一名本科生进行指导。迫切需要快速可靠地监测每种和多氟烷基物质(PFAS)的低成本方法。目前的PFAS分析依赖于与昂贵而笨重的质谱计检测器相结合的色谱方法。虽然这些方法有助于对全氟辛烷磺酸进行准确的低水平量化，但它们不是流动的，它们需要专门的培训来维护复杂的仪器。该项目的总体目标是建立一个自下而上的框架，用于开发可就地和在使用地点监测水中全氟辛烷磺酸污染的移动式、低成本的全氟辛烷磺酸传感平台。建议的框架将通过开发功能化微电极传感器阵列(MESA)平台并结合机器学习算法来演示，用于检测和定量不同水基质中具有一系列物理性质的全氟辛烷磺酸混合物。本项目的具体研究目标是：(1)利用电化学石英晶体微天平实验装置表征PFAS在吸附材料上的基本吸附/解吸机理；(2)利用计算密度泛函理论计算揭示控制PFAS在不同吸附材料上吸附/解吸的特定表面相互作用；(3)整合实验-计算结果以指导各种PFAS选择性、可逆吸附剂的选择；以及(4)构建和测试支持机器学习的PFAS检测台面平台。该项目的成功完成具有潜在的变革性影响，开发了一种传感器，用于选择性地检测全氟辛烷磺酸混合物中的个别化合物，检测限在低ng/L浓度范围内，并在不同的源水基质中具有可靠的性能。通过每年一度的本科生暑期研究体验和创建一个为期四周的研讨会向高中生介绍机器学习概念，将进一步造福社会。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.