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.

全氟烷基物质和多氟烷基物质 (PFAS) 是一组“永久化学品”，用于众多消费品和工业产品，包括不粘炊具、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。医学研究表明，接触极低水平的 PFAS 可能会导致婴儿长期发育障碍，并增加风险。因此，PFAS 污染是一个重要的环境问题，目前的 PFAS 检测方法需要昂贵的设备和专门的培训来维持复杂的仪器。该项目的目的是创建一种低成本的 PFAS 传感方法来监测水中的 PFAS 污染，这一目标将通过开发微电极传感器阵列与机器学习算法相结合来检测不同水源中的 PFAS 混合物来实现。该项目的成功完成将通过开发一种监测 PFAS 的低成本方法来造福社会。通过学生教育和推广，包括对伊利诺伊大学芝加哥分校的两名研究生和一名学生的指导，将给社会带来额外的好处。普渡大学的本科生。目前非常需要快速可靠地监测全氟烷基物质和多氟烷基物质 (PFAS) 的低成本方法，但目前的 PFAS 分析依赖于与昂贵且笨重的质谱检测器相结合的色谱方法。为了对 PFAS 进行精确的低水平定量，它们不可移动，并且需要专门的培训来维护复杂的仪器。该项目的总体目标是为开发移动、低成本 PFAS 传感创建一个自下而上的框架。可在现场和使用点监测水中 PFAS 污染的平台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 PFAS 混合物。该项目的具体研究目标是：(1) 使用电化学石英表征 PFAS 在吸附剂材料上的基本吸附/解吸机制。晶体微天平实验装置；（2）利用计算密度泛函理论计算来揭示控制不同吸附剂材料上的 PFAS 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为各种 PFAS 选择选择性、可逆吸附剂；(4) 制造和测试用于 PFAS 检测的机器学习 MESA 平台。该项目的成功完成有可能通过开发用于选择性检测的传感器来产生变革性影响。 PFAS 混合物中单个化合物的检测限在低 ng/L 浓度范围内，并且在不同水源水基质中具有可靠的性能，这将通过为本科生提供的年度夏季研究经验和举办为期四周的研讨会来实现。到向高中生介绍机器学习概念。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。