Facile identification of fluorosurfactant category in aqueous film-forming foam concentrates via optimized 19F NMR
Bibliographic record
Abstract
Aqueous film-forming foams (AFFFs) are the primary source of toxic perfluoroalkyl and polyfluoroalkyl substances (PFAS) in wastewater. Thus, it is urgent to develop a facile and fast method for identifying fluorosurfactants in commercially available AFFFs. In this work, fluorine nuclear magnetic resonance (19F NMR) spectroscopy was optimized to measure AFFFs directly with the extra addition of 5% D2O as the locking reagent, and high-quality spectra could be acquired within 4 min (0.1% fluorosurfactant content). Recovery experiments demonstrated that the use of different AFFFs had no marked influence on the quantitative analysis of fluorosurfactants. Such method works with low-field NMR spectroscopy (1.4 T) as well. Two-dimensional (2D) 19F COSY NMR was used to make signal assignments for different fluorosurfactant derivatives. The optimized 19F NMR could quantify the commercially available fluorosurfactants in different AFFFs, identify them being in either the perfluorooctane sulfonate (PFOS) or fluorotelomer sulfonic acid (FTS) categories, and distinguish the head-group of PFOS and FTS derivatives, which exhibits great potentials in the developments of relevant commercial detections.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".