POLYCYCLIC AROMATIC HYDROCARBONS: ULTRASENSITIVE DETECTION, EARLY LIFE EXPOSURES-CLINICAL OUTCOMES (PRETERM BIRTHS, CHRONIC LUNG DISEASE, AND NEUROCOGNITIVE DEFICITS), PREVENTION AND REMEDIATION

多环芳烃：超灵敏检测、生命早期暴露-临床结果（早产、慢性肺病和神经认知缺陷）、预防和补救

• 批准号: 10401127
• 负责人: BHAGAVATULA MOORTHY
• 金额: --
• 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 2021-07-24
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10401127
• 关键词: Air; Area; Aromatic Compounds; Aromatic Polycyclic Hydrocarbons; Benchmarking; Biological; Chemicals; Chemistry; Chronic lung disease; Clinical; Clutterings; Complex; Data; Data Science; Detection; Development; Effectiveness; Environment; Family; Foundations; Geometry; Goals; Health; Human; Image; Investigation; Machine Learning; Mass Fragmentography; Methods; Molecular; Monitor; Neurocognitive Deficit; Outcome; Parents; Partition Coefficient; Patient Monitoring; Polymers; Premature Birth; Prevention; Research; Sampling; Soil; Spectrum Analysis; Surface; Testing; Training; Variant; Visual; Water; Work; base; design; detection sensitivity; early life exposure; effectiveness testing; exposed human population; hazard; improved; innovation; interest; learning classifier; machine learning algorithm; machine learning method; monolayer; nanoparticle; remediation; spectroscopic survey; superfund site; surface coating; synergism; tool; training opportunity; vibration

Project Summary The overarching goal of this project is the development of new and innovative approaches to ultrasensitive detection and identification of polycyclic aromatic hydrocarbon (PAH) molecules and their functionalized derivatives (polycyclic aromatic compounds, or PACs). Ms. Mary Bajomo, will pursue research central to these project goals, as outlined by both Specific Aims. The Specific Aims of this Diversity Supplement form a central, essential subset of the work required to achieve the project goals, and provide an essential foundation for the sensing methods to be developed over the course of this project. They also lay the groundwork for bringing Machine Learning methods into the subfield of spectroscopic chemical sensing. Pursuing research at the interface between Chemistry and Machine Learning presents an exceptional training opportunity for Ms. Bajomo, and will allow her to interact strongly with the three PIs and their research groups in three fields: the Halas group, for experimental chemistry in the area of surface-enhanced spectroscopic sensing, the Nordlander group, on nanoparticle-based substrate design, and the Patel group, experts in Machine Learning and Data Science. Our hypothesis is that Machine Learning classifiers can be developed and used to distinguish between specific PAH and PAC molecules found in environmental or biological samples through their vibrational spectroscopic signatures. An essential aspect of this approach is the identification of PAH and PAC molecules while embedded in a molecular or polymer capture layer that has been designed to extract PAH/PAC molecules from environmental and/or biological samples that has its own specific spectroscopic signature “background”. These investigations are foundational to the detection of multicomponent mixtures of PAH/PAC molecules from realistic environmental or biological samples, using a combination of surface- enhanced spectroscopies and Machine Learning algorithms analogous to image recognition in cluttered, complex background environments. The two Specific Aims that Ms Bajomo will pursue are: Specific Aim 1: The identification and quantitative characterization of a universal capture layer for the wide range of PAH and PAC compounds encountered in biological and environmental samples. This capture layer would be serve as a universal coating for surface-enhanced Raman and Infrared spectroscopic substrates, and allow for the extraction of PAH and PAC molecules from solution in concentrations suitable for detection, consistent with concentrations of these chemicals found in samples of interest. Specific Aim 2: To develop surface-enhanced chemical sensing data as input to Machine Learning classifiers, to benchmark their effectiveness in identifying specific PAH molecules and distinguishing PAH/PAC molecules from each other by ML methods. This close synergy between experimental spectroscopic studies and ML classifier testing presents an outstanding opportunity for graduate training at the interface between two extremely important and dynamic research field for Ms. Bajomo.

项目总结