Potential of ozonation for the degradation of antibiotics in wastewater
Bibliographic record
Abstract
Increasing concern in recent years over the occurrence and fate of low-level concentrations of pharmaceuticals in the aquatic environment stimulates research on alternative treatment methods. This paper presents a study of the degradation of sulphamethoxazole, an antibiotic used on humans and animals in order to treat various bacterial infections, by ozonation. After 4.5 min of treatment, the concentration of sulphamethoxazole was below the HPLC detection limit of 0.6 mgL(-1), indicating degradation efficiency higher than 99.24%. This value is comparable and in some cases higher than published data on the degradation in drinking water. Kinetic analysis of the data indicated an overall first-order reaction with a rate constant of 1.0594 min(-1) at 20 degrees C. The reaction order differs with the second-order reaction observed by other researchers. This change of reaction order could be explained by the different treatment conditions used. Preliminary analysis using the FT-IR technique was also performed in order to obtain information on the structure of the degradation products. Further analysis using a GC-MS is needed in order to elucidate the structure of the degradation products. Finally, based on the experiments performed, ozonation seems to be a promising technique for the degradation of antibiotics, even in wastewater.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".