Nontargeted screening reveals fluorotelomer ethoxylates in indoor dust and industrial wastewater
Bibliographic record
Abstract
Concerns regarding the persistence, bioaccumulation behaviour, and toxicity of perfluorooctanoic acid and perfluorooctane sulfonic acid have resulted in the creation of thousands of replacement perfluoroalkyl substances (PFAS). This study reports on the discovery of fluorotelomer ethoxylates (FTEO) in indoor dust (9/15 samples), and industrial effluents (14/37 samples) using gas chromatographic cyclic ion mobility mass spectrometry (GC-cIMS). By filtering the detected unknowns by mass and collision-cross section, a series of FTEO homologues were revealed with the formula F-(CF2)n(C2H4O)xH, where n = 6,8,10, and x = 4–12. The highest concentrations were observed in samples collected from healthcare facilities, consistent with the potential use of these compounds in anti-fog products, sprays used to prevent condensation on eyeglasses. FTEOs were also detected in c. 40 % of industrial effluent samples, with the highest concentrations in electroplating facilities, manufacturers of cosmetics and personal care products, and linen cleaning services for healthcare and work uniforms. These results suggest that FTEOs may well be widespread pollutants that are more persistent than previously thought, underlining the need for further study of their occurrence and potential impact to human health and the environment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".