Comparative study on adsorption of perfluorooctane sulfonate (PFOS) and perfluorooctanoate (PFOA) by different adsorbents in water
Bibliographic record
Abstract
Perfluorinated compounds (PFCs) are emerging environmental pollutants. Perfluorooctane sulfonate (PFOS) and perfluorooctanoate (PFOA) are the two primary PFC contaminants that are widely found in water, particularly in groundwater. This study compared the adsorption behaviors of PFOS and PFOA on several commercially available adsorbents in water. The tested adsorbents include granular activated carbon (GAC: Filtrasorb 400), powdered activated carbon, multi-walled carbon nanotube (MCN), double-walled carbon nanotube, anion-exchange resin (AER: IRA67), non-ion-exchange polymer, alumina, and silica. The study demonstrated that adsorption is an effective technique for the removal of PFOS/PFOA from aqueous solutions. The kinetic tests showed that the adsorption onto AER reaches equilibrium rapidly (2 h), while it takes approximately 4 and 24 h to reach equilibrium for MCN and GAC, respectively. In terms of adsorption capacity, AER and GAC were identified as the most effective adsorbents to remove PFOS/PFOA from water. Furthermore, MCN, AER, and GAC proved to have high PFOS/PFOA removal efficiencies (≥98%). AER (IRA67) and GAC (Filtrasorb 400) were thus identified as the most promising adsorbents for treating PFOS/PFOA-contaminated groundwater at mg L(-1) level based on their equilibrium times, adsorption capacities, removal efficiencies, and associated costs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".