Compositional Effects on Leaching of Stain-Guarded (Perfluoroalkyl and Polyfluoroalkyl Substance-Treated) Carpet in Landfill Leachate
Bibliographic record
Abstract
Perfluoroalkyl and polyfluoroalkyl substances (PFASs) from stain-guard treated carpets in landfills continue to be released into the environment. To understand the leaching of PFASs from carpets to landfill leachate as a function of environmental factors, leaching concentrations of ten perfluoroalkyl carboxylic acids and four perfluoroalkyl sulfonic acids were quantified for different pHs, contact times, mixing speeds, and temperatures. Partitioning from carpet to leachate and distilled water at different pHs showed negligible differences. The total concentration of leaching PFASs in distilled water was approximately 1 ng L(-1) higher than in landfill leachate, indicating that the presence of multivalent cations in leachate could have a negative effect on leaching of PFASs. For all PFASs monitored, leaching increased with increasing contact time and temperature. Perfluorohexanoic and perfluoroheptanoic acids experienced the largest increases with contact time and temperature. Gibbs free energy (ΔG > 0), enthalpy (ΔH > 0), and entropy energy (ΔS < 0) indicated that PFAS leaching from carpet was dominantly controlled by entropy-driven processes and did not differ significantly among individual PFASs. PFAS concentrations in leachate with rotation of an end-over-end contactor were higher than under static conditions, but otherwise, varying the rotation speed had negligible influence. The results provide useful information for management of discarded stain-guard carpets in landfills.
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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 | 0.000 | 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) | 0.001 | 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".