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.

项目概要 接触多环芳烃 (PAH) 和相关的多环芳族化合物 多环芳烃（PACs）早已被认定对人类健康具有大量风险，是众所周知的致癌物质。 目前用于检测 PAH 和 PAC 的分析技术是基于实验室的、缓慢的、 复杂，并且需要昂贵的仪器和样品制备，我们提出了一种全新的方法。 结合光学光谱技术，例如表面增强拉曼光谱 (SERS) 和 表面增强红外吸收 (SEIRA) 也可以将这些技术组合到一起。 纳米工程基质，旨在灵敏地识别特定的 PAC，而这些技术已被采用。 成功地使用金和银基纳米颗粒和纳米工程基材，我们 建议使用廉价且环保的纳米工程铝来扩展这些技术 用于简化超灵敏 PAH 和 PAC 检测的底物 该平台将利用聚多巴胺（一种聚多巴胺）。 受贻贝粘附蛋白启发的仿生聚合物，作为分子分区的涂层，选择性地 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板上。 初步结果表明，该方法对从油中提取的 PAH 分子产生了亚 ppb 的检测灵敏度。 我们建议进一步设计和演示一种新型化学探测器。 与 SERS 和/或 SEIRA 基板完全集成，直接生成响应的电信号 PAH 和 PAC 分子的光谱，这将消除对体积大且昂贵的需求。 单色仪和色散光学器件，最终允许设计超紧凑的“片上”探测器 可以在超级基金现场和诊所中部署这种类型的直接光谱原型。 我们小组最近演示了探测器，我们还将解决主要问题之一。 普遍用于分析物检测和分析，化学混合物的检测，可能在实际情况下发现 通过应用机器学习方法，我们建议开发机器学习方法。 自动分析多组分样品光谱的算法，经过训练可识别高 其 PAH 和 PAC 组件的准确性和精确度 该项目的最终成果是创建了 精简、超紧凑、超灵敏的化学分析和检测平台，能够识别 单个样品中含有多种 PAH 和 PAC，无需昂贵的分离和纯化步骤，这可以 很容易过渡到现场使用。