Photocatalytic Treatment of Cibacron Brilliant Yellow 3G-P (Reactive Yellow 2 Textile Dye)
Bibliographic record
Abstract
The photocatalytic treatment of a textile dye wastewater, cibacron brilliant yellow 3G-P (reactive yellow 2), under the presence of UV-A radiation was studied. Treatment of test solutions containing 100 mg/L cibacron brilliant yellow dye with two types of photocatalysts, Degussa P25 and Hombikat UV 100 titanium dioxide, were investigated. The efficiency of these two commercial photocatalysts was compared in the presence and absence of hydrogen peroxide (H2O2). H2O2 concentration of 15 mM showed the best efficiency toward the photocatalytic treatment of the textile dye at the concentrations tested. Both decolorization and mineralization were significantly improved by using 15 mM H2O2. The treatment efficiency of the photocatalytic degradation of reactive yellow 2 was determined in terms of both adsorption kinetics and total mineralization. The decolorization and mineralization followed first order kinetics with a rate constant of 0.09 min(-1) for decolorization with Degussa P25 titanium dioxide in the presence of H2O2. 71.3% chloride and 27.9% sulphate were yielded after complete decolorization in the photocatalytic treatment of the dye. However, only 0.78% yield of the nitrate was obtained by photocatalysis. The formation of intermediates was not significant compared to the original dye solution in terms of absorbance.
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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 | 0.001 | 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".