PFAS Adsorption: A Direct Comparison between Colloidal and Powdered Activated Carbons from the Same Source Materials
Bibliographic record
Abstract
Granular, powdered, and colloidal activated carbons (GAC, PAC, and CAC, respectively) are being used to remove per- and polyfluoroalkyl substances (PFAS) from water. While previous studies have shown that the rate of PFAS adsorption on PAC was larger than on GAC, there has been no direct comparison of PFAS adsorption on PACs versus CACs. Further, there has been no investigation into the relationship between AC size and its ability to adsorb PFAS. To address these knowledge gaps, four commercial ACs (three GACs and one PAC) made from wood, coconut shells, and coal were pulverized by manual grinding followed by micromilling using a bench-top micronizing mill to create PACs (d50=12–107 μm) and CACs (d50=1.2–2.5 μm). The adsorption of either PFBS, PFOA, or PFOS on these adsorbents (11 in total) was investigated using batch solutions containing 0.02–0.2 g/L AC. Under the experimental conditions employed, ∼60% of the outcomes (43 out of 72) showed that less mass of the PFAS explored was adsorbed by CAC than the parent PAC. A detailed characterization of the adsorbent properties (surface area, micropore, and mesopore volumes; pHpzc; and surface elemental composition) suggests that the reduced adsorption capability of the CACs was likely the result of AC oxidation during milling, which decreased surface hydrophobicity. Of all AC types investigated, the materials made from wood possessed the greatest surface area and porosity but adsorbed PFAS the least. The repulsion between the negatively charged surface of wood-based ACs (pHpzc=4.7–5.1) and the negatively charged headgroup of the studied PFAS molecules was identified to be the dominant factor that inhibited adsorption. To our knowledge, this study is the first that systematically investigated the effect of particle size reduction on the ability of ACs of different source materials to adsorb PFAS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".