Identifying Unknown Fluorine-Containing Compounds in Environmental Samples Using <sup>19</sup>F NMR and Spectral Database Matching
Bibliographic record
Abstract
The ubiquity of per- and polyfluorinated alkyl substances (PFAS) in the environment is a continuing concern. While typical analytical methods for the analysis of PFAS include both targeted and non-targeted mass spectrometry, there remains a significant portion of fluorinated compounds that are not accounted for by these routine methods. It has been previously demonstrated that 19 F NMR can be used to identify these compounds, helping to close the mass balance on total fluorine in the environment. 19 F NMR offers an unbiased method of analysis that requires no anticipation of fluorine–carbon bonds or functional groups. However, there is resistance to further uptake of NMR spectroscopy as an analytical tool, owing to perceived difficulties in sensitivity and spectral overlap. In this study, we measure the 19 F NMR spectrum of hundreds of fluorinated compounds and use this constructed database to determine the concentration of PFAS in an extracted sample of a known aqueous firefighting foam-contaminated site. The 19 F NMR database has been included for use by other researchers, and we discuss the intricacies of 19 F NMR as applied to environmental samples.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".