Fluorinated Compounds in North American Cosmetics
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Per- and polyfluoroalkyl substances (PFAS), a highly persistent and potentially toxic class of chemicals, are added to cosmetics to increase their durability and water resistance. To assess this potential health and environmental risk, 231 cosmetic products purchased in the U.S. and Canada were screened for total fluorine using particle-induced gamma-ray emission spectroscopy. Of the eight categories tested, foundations, mascaras, and lip products had the highest proportion of products with high total fluorine ≥0.384 μg F/cm 2 . Twenty-nine products including 20 with high total fluorine concentrations were analyzed using targeted LC-MS/MS and GC-MS. PFAS concentrations ranged from 22–10,500 ng/g product weight, with an average and a median of 264 and 1050 ng/g product weights, respectively. Here, 6:2 and 8:2 fluorotelomer compounds, including alcohols, methacrylates, and phosphate esters, were most commonly detected. These compounds are precursors to PFCAs that are known to be harmful. The ingredient lists of most products tested did not disclose the presence of fluorinated compounds exposing a gap in U.S. and Canadian labeling laws. The manufacture, use, and disposal of cosmetics containing PFAS are all potential opportunities for health and ecosystem harm. Given their direct exposure routes into people, better regulation is needed to limit the widespread use of PFAS in cosmetics.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".