Noise-Reduced Quantitative Fluorine NMR Spectroscopy Reveals the Presence of Additional Per- and Polyfluorinated Alkyl Substances in Environmental and Biological Samples When Compared with Routine Mass Spectrometry Methods
Bibliographic record
Abstract
Per- and polyfluorinated alkyl substances (PFAS) are ubiquitous throughout the environment. Analysis of PFAS is commonly performed using both targeted and nontargeted mass spectrometry methods. However, it has been demonstrated that measurements of fluorinated compounds in the environment by mass spectrometry often fall short of the total fluorine concentration. In the present study, we employ a 19F NMR technique, which is capable of detailing fluorinated compounds in a sample while providing both quantitative and structural information. Inclusion of a noise-reduction strategy involving the acquisition of arrays of spectra with an increasing number of transients addresses the sensitivity challenges of environmental nuclear magnetic resonance (NMR), improving signal to noise. When this technique is applied to environmental and biological samples including rainwater, lake water, wastewater effluent, serum, and urine, the presence of PFAS, which may have been missed by routine mass spectrometric methods, is revealed. Important resonances in the 19F NMR spectrum such as that of trifluoroacetic acid are brought above the limit of quantification in all samples, allowing detection limits as low as 389 pg/L in rainwater. A liquid chromatography-tandem mass spectrometry (LC-MS/MS) method, which was used to analyze 47 PFAS compounds, accounts for only 3.7–27% of the total fluorine concentration as determined by the NMR strategy in the present study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.023 | 0.000 |
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 teacher head, 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".