Removal and transformation products of ibuprofen obtained during ozone- and ultrasound-based oxidative treatment
Bibliographic record
Abstract
The oxidation of ibuprofen (IBP) in water was evaluated using oxidative treatments: ozonation, sonication, hydrogen peroxide addition and combinations of these processes. After 20 minutes of treatment, ozone coupled with hydrogen peroxide at pH 7, 15 °C, an ozone dose of 16 mg/L and a hydrogen peroxide concentration of 7.1 mg/L was found to have the highest IBP (95%) and chemical oxygen demand (COD) (41%) removals. A synergistic effect was observed for the combined ozonation/sonication process, which might be explained by an improved mass transfer of ozone in the solution due to the presence of ultrasonic pressure waves. Transformation products were detected in the treated solutions. The nature of five of these products was confirmed by liquid chromatography-mass spectrometry (LC-MS), including 4-isobutylacetophenone (4-IBAP), oxo-IBP, 4-acetylbenzoic acid, 4-ethybenzaldehyde and oxalic acid. In addition, COD analyses for each experiment showed that the ratio of %COD removal to %IBP removal was highest with sonication; suggesting that this oxidative process offers other mechanisms of removal which may lead to further degradation of products formed. This study presents the first data on removal of IBP by sonication coupled to ozonation and provides some insight into the potential of this combined treatment approach for the removal of contaminants of emerging concern.
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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".