Catalytic ozonation of 2,4-Dichlorophenoxyacetic acid using alumina in the presence of a radical scavenger
Bibliographic record
Abstract
Using a laboratory-scale mixed reactor, the performance of alumina in degrading 2,4-Dichlorophenoxyacetic acid with ozone in the presence of tert-butyl alcohol radical scavenger was studied. The operating variables investigated were the dose of alumina catalyst and solution pH. Results showed that using ozone and alumina leads to a significant increase in 2,4-D removal in comparison to non-catalytic ozonation and adsorption processes. The observed reaction rate constants (k(obs)) for 2,4-D during ozonation were found to increase linearly with increasing catalyst dose. At pH 5, the k(obs) value increased from 19.3 to 26 M(-1) s(-1) and 67 M(-1) s(-1) when varying the alumina dose from 1 to 2 and 4 g L(-1), respectively. As pH was increased, higher reaction rates were observed for both non-catalytic ozonation and catalytic ozonation processes. Thus, at pH 3 and using a catalyst dose of 8 g L(-1), the k(obs) values for non-catalytic ozonation and catalytic ozonation processes were 3.4 and 58.9 M(-1) s(-1), respectively, whereas at pH 5 reaction rate constants of 6.5 and 128.5 M(-1) s(-1) were observed, respectively. Analysis of total organic carbon suggested that catalytic ozonation with alumina achieved a considerable level of mineralization of 2,4-D. Adsorption of 2,4-D on alumina was found to play an important role in the catalytic ozonation process.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".