Adsorption dynamics of four per-and polyfluoroalkyl substances (PFAS) onto activated sludge (AS) and aerobic granular sludge (AGS)
Bibliographic record
Abstract
Per- and polyfluoroalkyl substances (PFAS) are persistent contaminants of emerging concern, known for resisting removal in conventional biological wastewater treatment, like activated sludge (AS). In biological systems, adsorption is the primary mechanism for removing PFAS, creating a need to explore the adsorption extend of alternative treatments which can more effectively sorb PFAS. The primary goal of this study was to identify which sludge type achieves the highest adsorption capacity and, thus, the most effective PFAS removal. The experimental design encompassed on the adsorption dynamics of four representative PFAS compounds, a mix of two perfluorosulfonic acids (PFSAs) and two perfluoroalkyl carboxylic acids (PFCAs), using activated sludge and aerobic granular sludge (AGS) as sorbents. Results demonstrate that AGS exhibits a tenfold greater adsorption capacity for PFAS than AS, as evidenced by significantly higher K d values. While AS presented maximum K d values of −40, 32, 57 for PFPeA, PFOA and PFDS, the K d values using AGS were 254, 205 and 207, respectively. PFDS was the compound with the highest adsorption affinity in both sorbents, achieving K d values of approx. 11000 for AS and 71000 for AGS. This discrepancy in adsorption capacity could be attributed to AGS’ granular structure, which may trap PFAS more effectively and the high surface hydrophobicity, which allows for stronger hydrophobic interactions with PFAS molecules. These findings underscore the prospective of aerobic granular sludge as an enhanced treatment technology for PFAS separation in WWTPs. • PFDS had the highest adsorption, indicating strong retention of long-chain PFAS by sludge. • AGS adsorbed up to 10 × more PFAS than AS, highlighting its potential for improved removal. • AGS followed a multilayer isotherm, suggesting its structure enhances PFAS adsorption. • Negative Kd values showed significant PFAS desorption from AS, posing a risk of re-release. • The impact of synthetic wastewater vs. Milli-Q varied depending on the order of testing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".