Project 1: Streamlined identification of PAHs/PACs in environmental samples using ultracompact spectroscopy platforms and machine learning strategies

项目 1：使用超紧凑光谱平台和机器学习策略简化环境样品中 PAH/PAC 的识别

• 批准号: 10116392
• 负责人: NAOMI HALAS
• 金额: $ 27.2万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-28 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10116392
• 关键词: Address; Air; Algorithms; Aluminum; Aromatic Compounds; Aromatic Polycyclic Hydrocarbons; Biological; Biomimetics; Carcinogens; Chemicals; Chronic lung disease; Classification; Clinic; Clinical; Complex; Complex Mixtures; Detection; Development; Devices; Electromagnetics; Evaluation; Exposure to; Family; Geometry; Goals; Gold; Health; Health Hazards; Human; Hydroxyl Radical; Laboratories; Libraries; Liquid substance; Machine Learning; Manuals; Mass Fragmentography; Methods; Modeling; Molecular; Molecular Structure; Monitor; Mutagens; Nanostructures; Neurocognitive Deficit; Optics; Outcome; Parents; Patient Monitoring; Polymers; Population; Premature Birth; Preparation; Prevention; Raman Spectrum Analysis; Research; Risk; Risk Assessment; Sampling; Signal Transduction; Silicon; Silver; Soil; Spectrum Analysis; Structure; Surface; Techniques; Technology; Testing; Time; Training; Variant; Water; Work; absorption; adhesive protein (mussel); air sampling; automated algorithm; base; convolutional neural network; cost; design; detection limit; detection method; detection platform; detection sensitivity; detection test; detector; early life exposure; enhancing factor; exposed human population; hazard; improved; infrared spectroscopy; innovation; instrumentation; interest; learning algorithm; learning strategy; machine learning algorithm; metallicity; model development; monolayer; nanoengineering; nanofabrication; nanoparticle; nanosensors; novel strategies; prototype; remediation; response; soil sampling; superfund site; tool; vibration; water sampling; waterborne

Project Summary Exposure to polycyclic aromatic hydrocarbons (PAHs) and associated polycyclic aromatic compounds (PACs) has long been identified with a large number of human health risks. PAHs are well-known carcinogens and mutagens. Current analytical techniques for detection of PAHs and PAC are laboratory based, slow, complex, and require expensive instrumentation and sample preparation. We propose an entirely new approach combining optical spectroscopic techniques such as Surface Enhanced Raman Spectroscopy (SERS) and Surface Enhanced Infrared Absorption (SEIRA). These techniques can also be combined onto a single nanoengineered substrate, designed to sensitively identify specific PACs. While these techniques have been demonstrated successfully using gold and silver based nanoparticles and nanoengineered substrates, we propose to expand these techniques using inexpensive and environmentally friendly Aluminum nanoengineered substrates for streamlined ultrasensitive PAH and PAC detection. This platform will utilize polydopamine, a biomimetic polymer inspired by mussel adhesive proteins, as coatings for molecular partitioning, selectively extracting and adsorbing PAH and PAC molecules from samples of interest onto the nanosensing substrates. In preliminary results, this approach has yielded sub-ppb detection sensitivities for PAH molecules extracted from liquid samples. Furthermore we propose to design and demonstrate a new type of chemical detector that can be fully integrated with SERS and/or SEIRA substrates, to directly generate an electrical signal in response to the spectrum of the PAH and PAC molecules. This would eliminate the need for bulky and expensive monochromators and dispersive optics, ultimately allowing for the design of ultracompact, “on-chip” detectors that can be deployed in the field at superfund sites and in the clinic. Prototypes of this type of direct spectral detector have recently been demonstrated by our group. We will also address one of the primary problems universal to analyte detection and analysis, the detection of chemical mixtures, likely to be found under actual field sampling conditions, by applying a machine learning approach. We propose to develop machine learning algorithms that automatically analyze the spectra of multicomponent samples, trained to identify with high accuracy and precision their PAH and PAC components. The ultimate outcome of this project is the creation of a streamlined, ultracompact, ultrasensitive chemical analysis and detection platform, capable of identifying multiple PAHs and PACs in a single sample without costly separation and purification steps, which could be readily transitioned to fieldable use.

项目摘要 接触多环芳烃和相关的多环芳烃化合物 （PAC）长期以来被确定具有大量的人类健康风险。多环芳烃是众所周知的致癌物质 和诱变剂。目前用于检测多环芳烃和PAC的分析技术是基于实验室的，缓慢， 复杂，并且需要昂贵的仪器和样品制备。我们提出了一个全新的方法 结合光学光谱技术如表面增强拉曼光谱（Sers）和 表面增强红外吸收（SEIRA）这些技术也可以结合到一个单一的 纳米工程基板，旨在灵敏地识别特定的PAC。虽然这些技术已经 成功地证明了使用金和银基纳米粒子和纳米工程基板，我们 我建议扩大这些技术使用廉价和环境友好的铝纳米工程 用于流线型超灵敏PAH和PAC检测的基质。该平台将利用聚多巴胺， 仿生聚合物的灵感来自贻贝粘附蛋白，作为涂层的分子分配，选择性 从感兴趣的样品中提取PAH和PAC分子并将其吸附到纳米传感基底上。在 初步结果，这种方法已经产生了亚ppb的检测灵敏度为PAH分子提取， 液体样品。此外，我们建议设计和演示一种新型的化学探测器， 与Sers和/或SEIRA基底完全集成，以响应于 PAH和PAC分子的光谱。这将消除对笨重和昂贵的 单色器和色散光学器件，最终允许设计超紧凑的“片上”检测器 可以部署在超级基金现场和诊所。这种类型的直接光谱的原型 我们小组最近演示了一个探测器。我们还将解决一个主要问题， 通用于分析物的检测和分析，检测化学混合物，在实际中可能发现 现场采样条件，通过应用机器学习方法。我们建议发展机器学习 自动分析多组分样品光谱的算法，经过训练， PAH和PAC成分的准确度和精密度。该项目的最终成果是创建 一个流线型、超紧凑、超灵敏的化学分析和检测平台，能够识别 在单一样品中检测多种PAH和PAC，无需昂贵的分离和纯化步骤， 易于转换为现场使用。