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Title: Increasing Confidence in Resolving Isomers of Emerging Per-And Polyfluoroalkyl Substances (PFASs) by Using DFT and Cosmo-Rs to Predict Chromatographic Retention Factors
Per-and polyfluoroalkyl substances (PFAS) are a class of contaminants of emerging concern frequently used in products like aqueous firefighting foams and non-stick coatings due to their stability and surfactant-like qualities. The lack of analytical standards for many emerging PFAS have severely limited our ability to comprehensively identify unknown PFAS contaminants in the environment, especially those that occur as isomers. Annotation of small molecules and identification of unknowns based only on elemental composition and mass fragmentation patterns remain major challenges in nontarget analysis employing liquid chromatography with high-resolution mass spectrometry (LC-HRMS). In this study, chromatographic retention factors (k) and mass spectral fragmentation patterns of 32 known PFAS were determined using our optimized parameters in LC-HRMS. The same method was then used to analyze previously unidentified PFAS in actual environmental samples. Using characteristic ions observed in the MS fragmentation of PFAS, the most probable isomeric structures of the detected PFAS were predicted. To increase confidence in the predicted molecular structure, Density Functional Theory and Conductor-like Screening Model for Realistic Solvents (COSMO-RS) calculations were used to predict physicochemical properties of different constitutional isomers. The DFT calculations facilitated geometric optimization, determination of polarizability, and calculation of the chemical potential the isomers. COSMO-RS uses the chemical potential to predict thermodynamic properties of molecules such as pKa, solubility, and Kow. These properties were then used to make a multi-variable linear regression to predict k values. The model was trained using 32 known PFAS. The properties used were log Kow of the neutral and anion species of the PFAS, and their polarizability. The model was specific enough to predict significantly different k values of unknown compounds with similar structures, which facilitated assignment of isomeric structures of PFAS.  more » « less
Award ID(s):
1904825
NSF-PAR ID:
10345232
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Annual meeting abstracts
ISSN:
1087-8939
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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