Enhancing Interface Reactions by Introducing Microbubbles into a Plasma Treatment Process for Efficient Decomposition of PFOA
Bibliographic record
Abstract
Efficient destruction of perfluoroalkyl compounds in contaminated waters remains a challenge because of highly stable C–F bonds. In this study, mineralization of perfluorooctanoic acid (PFOA) with high concentration (∼30 mg/L) was realized in a needle–plate pulsed discharge reactor integrated with a water jet (NPDW) to which microbubbles (MBs) with different carrier gases (air, N 2, and Ar) were introduced to enhance interfacial reactions. MBs effectively enrich dispersed PFOA from a bulk solution to a liquid surface to allow enhancing contact with reactive species and also expanding the plasma discharge area and channels. The PFOA removal efficiency in air and Ar discharge reached 81.5 and 95.3% in 2 h, respectively, with a defluorination ratio of no less than 50%. Energy requirements (EE/O) ranged from 216.49 to 331.95 kWh/m 3 . Aside from fluoride, PFOA was degraded to a range of short-chain perfluoroalkyl acids and, to a minor extent, at least 20 other fluorinated transformation products. PFOA degradation mechanisms were proposed, including decarboxylation, hydroxylation, hydrogenation reduction, and defluorination reactions. Real water matrices (groundwater, tap water, wastewater effluent, and surface water) showed moderate impact on treatment outcomes, demonstrating the robustness of the treatment process. The study demonstrated an environmentally friendly nonthermal plasma technology for effective PFOA degradation.
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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 | 0.000 |
| 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".