Removal of PFAS by hydrotalcite: Adsorption mechanisms, effect of adsorbent aging, and thermal regeneration
Bibliographic record
Abstract
Layered double hydroxides (LDH) have been shown to be effective adsorbents, but their utility for the treatment of per- and polyfluoroalkyl substances (PFAS) in water has not been fully explored. In this study, the adsorption of 9 PFAS on hydrotalcite (HT), a type of LDH, was investigated using reaction solutions with environmentally relevant PFAS concentrations. The adsorption of individual PFAS by HT depended upon a range of factors, including the temperature used to pre-treat (i.e., calcine) the HT, aging conditions, and the presence of anions in the solution. HT calcined near 400 °C most effectively adsorbed PFAS, but its ability to adsorb PFAS was sensitive to storage conditions. The adsorption of CO2 and moisture from air, which likely resulted in the re-intercalation of CO32− into the interlayer regions of HT, was observed to reduce PFAS adsorption and may explain performance loss over time. The adsorption trend among 9 PFAS and the influence on this process by Cl−, NO3−, SO42−, and CO32− indicated that adsorption occurred via a combination of ion exchange, electrostatic attraction, and hydrophobic interactions, although the relative importance of each mechanism deserves further investigation. During this study, we also demonstrated for the first time that HT can be thermally regenerated at 400 °C without affecting its ability to adsorb PFOS and PFBA. Overall, our results suggest that HT may serve as an effective alternative for PFAS treatment.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".