Photocatalytic degradation of Reactive Black 8 in UV/TiO2 /H2O2 system: Optimization and modeling using a response surface methodology (RSM)
Bibliographic record
Abstract
Reactive azo dyes are among the most applicable dyes n textile industries. However, these compounds are environmentally hazardous and difficult to treat by classical ethods. In the present study a batch stirred photoreactor with a novel irradiating setup was utilized for decolorization of a reactive azo dye. The effects of certain parameters including initial dye, H2O2 concentration, photocatalyst loading and initial pH in a system consisting of UV/TiO2/ H2O2 on the decolorization and degradation of C.I. Reactive Black 8 were investigated. Box-Behnken method, a statistical experimental design method, has been used to optimize the decolorization process. Decolorization efficiency increases with decreasing initial dye concentration, addition of H2O2 up to an optimum value, increasing photocatalyst loading and at original pH values (containing no pH adjusting chemicals). The optimum parameters for decolorization of Reactive Black 8 are obtained as follows: TiO2 = 1.59 g L-1, Reactive Black 8 concentration =34.65 ppm, pH = 5.5, H2O2 concentration = 1.82 (stoichiometric ratio). By use of these optimum parameters 96.1% decolorization and 78.6% degradation of the dye in the solution was observed within 60 minutes irradiation.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".