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

> **NIH NIH P42** · BAYLOR COLLEGE OF MEDICINE · 2021 · $1

## Abstract

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 g...

## Key facts

- **NIH application ID:** 10401127
- **Project number:** 3P42ES027725-02S3
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** BHAGAVATULA MOORTHY
- **Activity code:** P42 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1
- **Award type:** 3
- **Project period:** 2020-02-28 → 2021-07-24

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10401127

## Citation

> US National Institutes of Health, RePORTER application 10401127, POLYCYCLIC AROMATIC HYDROCARBONS: ULTRASENSITIVE DETECTION, EARLY LIFE EXPOSURES-CLINICAL OUTCOMES (PRETERM BIRTHS, CHRONIC LUNG DISEASE, AND NEUROCOGNITIVE DEFICITS), PREVENTION AND REMEDIATION (3P42ES027725-02S3). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10401127. Licensed CC0.

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