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.
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Quantitative Structure Retention Relationships to Identify Per-/Poly-Fluoroalkyl Substances and Other Contaminants of Emerging Concern
Nontarget analysis using liquid chromatography–high resolution mass spectrometry (LC–HRMS) is a valuable
approach in characterizing for contaminants of emerging concern (CECs) in the environment. However,
identification of these analytes can be quite costly or taxing without proper analytical standards. To circumvent
this problem we utilize Quantitative structure-retention relationships (QSRR) models to predict elution order
and retention times. Properties calculated from density functional theory (DFT) and the conductor-like
screening model for real solvents (COSMO-RS) theory are used to produce our QSRR models, which can be
calculated for virtually any analyte. We show that this methodology has been successful in identification of per-
/poly-fluoroalkyl substances (PFAS) and other contaminants. Nontarget analysis using liquid chromatography–
high resolution mass spectrometry (LC–HRMS) is a valuable approach in characterizing for contaminants of
emerging concern (CECs) in the environment. However, identification of these analytes can be quite costly or
taxing without proper analytical standards. To circumvent this problem we utilize Quantitative structureretention
relationships (QSRR) models to predict elution order and retention times. Properties calculated from
density functional theory (DFT) and the conductor-like screening model for real solvents (COSMO-RS) theory
are used to produce our QSRR models, which can be calculated for virtually any analyte. We show that this
methodology has been
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- Award ID(s):
- 1904825
- NSF-PAR ID:
- 10345229
- Date Published:
- Journal Name:
- Annual meeting abstracts
- ISSN:
- 1087-8939
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Chromatographic retention times and mass spectrometral fragmentation of per- and polyfluoroalkyl substances (PFASs) standards were determined using the optimized parameters obtained for liquid chromatography with tandem high-resolution mass spectrometry (LC-HRMS) analysis. Characteristic fragment ions obtained at various collision energies (MS2 fragmentation) were used for structural elucidation to predict the identities of newly discovered (emerging) PFASs detected in environmental samples. Moreover, the COnductor-like Screening MOdel for Realistic Solvents (COSMO-RS) was used to calculate the octanol-water partition coefficients (Kow) and mean isotropic polarizabilities of known PFASs, and the values were plotted against their chromatographic retention factors (k) to obtain a multivariable regression model that can be used to predict k values of unknown PFASs. Retention factor values of different structural isomers of the unknown PFASs were calculated and compared to the experimental k. For all the unknown PFASs, the predicted k value for the isomer that matches the corresponding MS2 fragmentation was found to be within 5% of the experimentally measured k value. This study demonstrates the applicability of a simple approach that combines the use of computationally-derived log Kow and polarizabilities, experimentally-determined k values, together with observed MS2 fragmentation patterns, in assigning the structures of emerging PFASs at environmentally relevant conditions when no reference standards are available.more » « less
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Results The optimized unknown screening workflow using a search index threshold of 750 resulted in positive identification of 70 analytes in validation samples, and a reduction in the number of false positives by over 50%. An average of 26 compounds with high confidence identification, 7 level 1 compounds and 19 level 2 compounds, were observed in worn wristbands. The data were further analyzed via suspect screening and retrospective suspect screening to identify an additional 36 compounds.
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Conclusions We have developed a sensitive, precise method for measuring the natural‐abundance sulfur isotopic compositions of cysteine and methionine isolated from biological samples. This capability opens up diverse applications of sulfur isotopes in amino acids and proteins, from use as a tracer in organisms and the environment, to fundamental aspects of metabolism and biosynthesis.
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