Plasma water treatment for PFAS: Study of degradation of perfluorinated substances and their byproducts by using cold atmospheric pressure plasma jet
Bibliographic record
Abstract
This study evaluates the effectiveness of non-thermal plasma at atmospheric pressure (NTP APPJ) for treating PFAS - contaminated water in different matrices. Successful removal of several perfluoroalkyl carboxylic acids (PFCAs) (C6 to C4), perfluroalkane sulfonic acids (PFSAs) (C8 to C4) and perfluropolyethers (PFPEs) (GenX and ADONA) PFAS compounds was achieved in laboratory scale experiments. In the deionized water (DW), high removal efficiencies (> 90%) were observed for longer-chain PFAS, PFOS (99.89%), PFHxA (94.61%) and ADONA (94.83%), while shorter- chain compounds had lower removal rates. Real water samples (tap water and synthetic effluent) showed lower overall degradation percentages (8–50%) depending on compound and matrix. Short-chain PFAS displayed around 10% removal in tap water , while PFOS and GenX achieved 50% and 32% removal, respectively. Complex matrix effects influence degradation rates. Byproducts from the plasma treatment were investigated, revealing distinct degradation mechanisms for various PFAS compounds. For PFSAs and PFCAs, degradation involved electron transfer , bond breaking and subsequent reactions. Conversely, ADONA and GenX degradation initiated with ether-group cleavage, followed by additional transformation processes. Plasma-based technology shows potential for degradation of PFAS, especially for newer substitute compounds like ADONA and GenX. However, further research is needed to optimize plasma performance for complete mineralization of PFAS. This study also proposes a degradation mechanism for ADONA, marking a novel investigation into ether-group PFAS degradation with potential implications for further research and understanding toxicological implications.
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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) | 0.000 | 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".