Determination of Perfluorinated Surfactants in Surface Water Samples by Two Independent Analytical Techniques: Liquid Chromatography/Tandem Mass Spectrometry and <sup>19</sup>F NMR
Bibliographic record
Abstract
Perfluorinated surfactants are an important class of specialty chemicals that have received recent attention as a result of their persistence in the environment. Two analytical methods for the determination of perfluorinated surfactants in aqueous samples were developed in order to investigate a spill of 22000 L of fire retardant foam containing perfluorinated surfactants into Etobicoke Creek (Toronto, Ontario). With the first method, aliquots of surface water (0.2-200 mL) were preconcentrated using solid-phase extraction. Liquid chromatography/tandem mass spectrometry was employed for identification and quantification of each perfluorinated surfactant. Total perfluorinated surfactant concentrations in surface water samples ranged from 0.011 to 2270 microg/L, and perfluorooctanesulfonate was the predominant surfactant observed. Interestingly, perfluorooctanoate was detected in surface water sampled upstream of the spill. A second method employing 19F NMR was developed for the determination of total perfluorinated surfactant concentrations in aqueous samples (2-100 mL). By 19F NMR, the surface water concentrations ranged from nondetect (method detection limit, 10 microg/L for a 100-mL sample) to 17000 microg/L. These methods permit comprehensive evaluation of aqueous samples for the presence of perfluorinated surfactants and have applicability to other sample matrixes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".