New Insights into the Degradation Mechanism of Perfluorooctanoic Acid by Persulfate from Density Functional Theory and Experimental Data
Bibliographic record
Abstract
Thermally activated persulfate is a promising oxidant for in situ remediation of perfluorooctanoic acid (PFOA), yet a comprehensive understanding of the degradation mechanism is still lacking. In this study, we used density functional theory (DFT) calculations and experimental data to map entire reaction pathways for the degradation of PFOA by persulfate, with specific considerations on the influence of pH. The DFT results showed that the rate-limiting step was the first electron abstraction from PFOA, yet the generation of SO 4 •– from the decomposition of persulfate contributed a large part of the free energy of activation (Δ G ‡ ) for the overall reaction. The subsequent steps did not contribute to the Δ G ‡ . For the electron abstraction from PFOA, we investigated reactions using protonated and deprotonated species of PFOA and SO 4 •– and showed that the reaction of anionic PFOA with HSO 4 • was most favorable with a Δ G ‡ of 7.2 kJ/mol. This explains why low pH (<3.5) is a sine qua non condition for the degradation of PFOA by persulfate. The overall Δ G ‡ derived theoretically based on the pathway involved HSO 4 • was consistent with the Δ G ‡ determined experimentally. This study provides valuable insight into remediation strategies that include persulfate as an oxidizing agent for perfluoroalkyl carboxylic acids.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".