# Resolving Relationships: Determining the Impacts of Environmental Matrices on the Ionization Efficiencies of Per and Polyfluoroalkyl Substances (PFAS) for the Development of a Semi-Quantitation Model

> **NIH NIH R01** · STATE UNIVERSITY OF NEW YORK AT BUFFALO · 2022 · $15,998

## Abstract

Abstract:
 A number of analytical challenges hinder the accurate identification and quantification of
per and polyfluoroalkyl substances (PFAS) in the environment. These challenges include the lack
of reference materials for PFAS as well as the ionization variability due to matrix interferences in
electrospray ionization. In this externship, I plan on addressing these challenges by evaluating
major matrix characteristics of environmental water samples and their relationship to the ionization
efficiency. The observed relationships will be used to develop a preliminary statistical model for
the semi-quantitation of PFAS without reference materials. Through collaboration with the United
States Environmental Protection Agency, I will gain expertise from mentors, Dr. Sobus and Dr.
McCord, in using cheminformatics for solving analytical problems. The semi-quantitation model
developed as a result of the externship can be used to quantify transformation products of PFAS
degradation studies, such as the nano bio-remediation techniques designed under the parent NIH
grant, R01ES032717.

## Key facts

- **NIH application ID:** 10580971
- **Project number:** 3R01ES032717-02S1
- **Recipient organization:** STATE UNIVERSITY OF NEW YORK AT BUFFALO
- **Principal Investigator:** Diana S Aga
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $15,998
- **Award type:** 3
- **Project period:** 2022-08-01 → 2022-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10580971

## Citation

> US National Institutes of Health, RePORTER application 10580971, Resolving Relationships: Determining the Impacts of Environmental Matrices on the Ionization Efficiencies of Per and Polyfluoroalkyl Substances (PFAS) for the Development of a Semi-Quantitation Model (3R01ES032717-02S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10580971. Licensed CC0.

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