Nonpolymeric Per- and Polyfluoroalkyl Substances (PFAS) and Side-Chain Fluorinated Polymers in Canadian Building Products
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide To understand if exterior building products are a source of perfluoroalkyl and polyfluoroalkyl substances (PFAS) to the environment, we measured PFAS in 145 samples of paints, sealers, and textiles, mostly intended for exterior use. We first developed a combined one and two-dimensional diffusion-ordered spectroscopy (1D and 2D-DOSY) fluorine nuclear magnetic resonance ( 19 F NMR) method to characterize and quantify side-chain fluorinated polymers (SCFPs) and nonpolymeric PFAS. Results showed that 36% (22/61), 24% (13/54), and 70% (21/30) of tested paints, sealers, and textiles contained total organic fluorine (TOF) at 0.1–16.1, 0.4–2488, and 0.34–74.0 μmol/g, respectively. Paints mainly contained 6:2 fluorotelomer (FT) and aromatic-CF 3 nonpolymeric PFAS, while sealers also contained 6:2 FT SCFPs and perfluorobutane sulfonyl fluoride (PBSF)-based PFAS. Textiles contained 6:2 FT, 8:2 FT, and PBSF-based PFAS indicative of SCFPs. Advanced mass spectrometry methods revealed that paints and sealers with 6:2 FT nonpolymers mainly contained 6:2 fluorotelomer phosphate mono-, di-, and triesters (6:2 mono-, di-, and triPAPs) and 6:2 FT dipyrophosphate, while textiles primarily contained n:2 fluorotelomer alcohols (n:2 FTOH, n = 6, 8) or N -methyl perfluorobutane sulfonamide alcohol (MeFBSE). Based on the PFAS found in these products and the recommendation to reapply the coatings to protect exterior/interior surfaces and the replacement of exterior textiles, these results indicate that building products likely contribute polymeric and nonpolymeric PFAS to 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 | 0.001 |
| 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) | 0.002 | 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".