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.

项目总结