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

项目摘要 暴露于多环芳烃(PAHs)和相关的多环芳烃 (PAC)长期以来一直被认为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PAHs和PAC的分析技术是基于实验室的、慢速的、 复杂，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，如表面增强拉曼光谱(SERS)和 表面增强红外吸收(SEIRA)。还可以将这些技术组合到单个 纳米工程衬底，旨在敏感地识别特定的PAC。虽然这些技术已经被 我们成功地展示了使用基于金和银的纳米颗粒和纳米工程衬底 建议使用廉价和环境友好的纳米工程铝来扩展这些技术 用于流线型超灵敏PAH和PAC检测的底物。这个平台将利用聚多巴胺，一种 由贻贝黏附蛋白激发的仿生聚合物，选择性地作为分子分配的涂层 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衬底上。在……里面 初步结果表明，该方法对从 液体样本。此外，我们还建议设计并演示一种新型的化学探测器，它可以 与SERS和/或Seira衬底完全集成，以直接生成响应于 PAH和PAC分子的光谱。这将消除对笨重和昂贵的需求 单色仪和色散光学，最终允许设计超紧凑型“片上”探测器 它可以部署在现场、超级基金站点和诊所。这种类型的直接光谱的原型 探测器最近已由我们的小组演示。我们还将解决其中一个主要问题 通用型对分析物的检测和分析，对化学混合物的检测，很可能在实际情况下发现 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的光谱的算法，经过训练以识别高 准确度和精密度它们的PAH和PAC组件。这个项目的最终结果是创建 一个流线型、超小型、超灵敏的化学分析和检测平台，能够识别 单个样品中的多个PAHs和PAC，无需昂贵的分离和纯化步骤，这可能是 很容易转变为可现场使用。

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.