Effectiveness of ozonation and catalytic ozonation (iron oxide) in the degradation of sunset yellow dye
Bibliographic record
Abstract
Abstract Azo dyes present in industrial effluents represent a hurdle that regular treatments cannot overcome. In this study, the application of ozone and a catalytic (iron oxide) ozone treatment were proposed as a means of degrading aqueous sunset yellow dye. In order to understand the factors involved, a rotatable central composite design was applied using the variables time, initial dye concentration (C0), pH, ozone inlet concentration (O3), and mass of catalyst, which varied in each case. All variables were significant in colour removal. Extremes in pH, lower C0, and higher ozone concentrations are conditions that favour dye degradation. A complete colour loss occurred for certain combinations of these parameters. The application of iron oxide as a catalyst did not present a satisfactory improvement in the reaction rate. Chemical oxygen demand and total organic carbon showed minimum values of 80% and 78%, respectively, for the worst experimental conditions (pH 7.0, C0 of 45 mg · L−1, and 5 g O3 · m−3), while their values were 88% and 83% for the best conditions applied. There was no immobilization of Artemia salina nauplii, even for the experimental run where the maximum concentration of dye of the set was used (45 mg dye · L−1). Ozonation is a promising alternative in the degradation of aqueous sunset yellow dye, being favoured in acidic or basic media, which is especially important since food effluents usually present low pH and show low toxicity. The mathematical model proposed can be useful in the design of wastewater treatment processes.
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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) | 0.000 | 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".