Quantitative Measurement of TFA and Other Short Chain PFAS in Environmental Samples Using <sup>19</sup> F SSFP-CRAFT NMR
Bibliographic record
Abstract
NMR has long been perceived to have relatively poor sensitivity vs other methods. However, NMR can be highly useful for examining complex samples. Fluorine NMR has been routinely demonstrated in the literature to be a powerful discovery tool in the analysis of per- and polyfluorinated alkyl substances (PFAS) in a variety of environmental and biological samples. In this study, we adapt the previously published steady-state free precession (SSFP) NMR with non-Fourier transform data analysis (complete reduction to amplitude frequency tables (CRAFT)) for quantitative analysis of ultrashort chain fluorinated acids. SSFP-CRAFT results have challenged the perception that NMR is sensitivity-limited but has not yet been shown to be fully quantitative. Adapting the SSFP-CRAFT approach for quantitative measurement of TFA, PFMeS, and other selected PFAS is a crucial step in further understanding environmental contamination from these species. Sensitivity is improved over conventional NMR using rapid radiofrequency pulses to collect hundreds of thousands of scans in short experiment times, allowing for instrument detection limits as low as 0.16 μg L –1 TFA in aqueous samples under fully quantitative NMR conditions. The quantitative 19 F SSFP-CRAFT NMR method is used to measure concentrations of TFA and other PFAS in four real world samples: drinking water, Arctic surface waters, human serum, and plants intended for human consumption. TFA was found and quantified in all samples. The very high concentration of TFA in the leaves of spinach (1540 ng g –1 ) points to a potential exposure pathway partially explaining higher than expected TFA concentrations in human serum, at 19.5 ng mL –1 .
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".