Sorption of per- and polyfluoroalkyl substances (PFASs) on filter media: Implications for phase partitioning studies
Bibliographic record
Abstract
Per- and polyfluoroalkyl substances (PFASs), including perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS), are ubiquitous in the environment. Investigations into their fate and potential phase-partitioning behavior require separating solid from aqueous phases via filtration. However, sorption of aqueous-phase PFASs on filtration media may lead to underestimation of PFAS concentrations in the aqueous phase. The authors investigated the sorption of perfluoroalkyl carboxylates, perfluoroalkyl sulfonates, perfluoroalkyl phosphonic acids, perfluoroalkyl phosphinic acids (PFPiAs), polyfluoroalkyl phosphate monoesters, polyfluoroalkyl phosphate diesters (diPAPs), fluorotelomer sulfonates, and perfluorooctane sulfonamide on filtration media. The effects of concentration (3 spiking levels), filter media (4 types), matrix (4 matrices), and compound structure on sorption are reported. Glass fiber filtration resulted in the least sorption, whereas polytetrafluoroethylene filters resulted in the most sorption (up to 98%). Analyte concentration had no significant effect. Sorption was generally consistent across matrix types except for samples affected by aqueous film forming foam deployment, which displayed high sorption of PFOS on nylon filters. Sorption usually increased with an increasing number of carbon or fluorine atoms and was most pronounced for PFPiAs and diPAPs (30–75% sorption). Overall, glass fiber filters are more recommended than nylon filters in environmental samples when phase separation is required. Use of filtration media for PFAS must be preceded by matrix-specific testing to account for unpredictable effects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".