Removal of reactive azo dyes from water by zero-valent iron reduction followed by peroxidase-catalyzed polymerization.
Bibliographic record
Abstract
Removing reactive azo dyes from textile wastewater is a significant challenge due to their color, non-biodegradability and toxicity. Although various treatment methods are available, it was hypothesized that reduction by zero-valent Fe followed by enzymatic treatment could be an environmentally friendly and cost effective approach. Zero-valent Fe cleaves the azo bond, reducing the dyes to aromatic amines, which are then oxidized and polymerized by enzymatic treatment. Finally, these polymers are removed by coagulant (PEI) aided sedimentation and filtration. The effectiveness of zero-valent Fe and Arthromyces ramosus peroxidase (ARP) treatment in the proposed process were studied on two representative reactive azo dyes, Reactive Red 2 (RR2) and Reactive Black 5 (RB5). To gain insight into ARP action on these two dyes, two model compounds, diphenylamine (DPA) and 2-amino-8-naphthol-3,6-disulfonic acid (ANDSA) were studied. A comparison with other treatment methods asserted the superior advantage of the proposed process in terms of actual pollutant and colour removal. (Abstract shortened by UMI.) Source: Masters Abstracts International, Volume: 43-03, page: 0919. Advisers: J. K. Bewtra; K. E. Taylor. Thesis (M.A.Sc.)--University of Windsor (Canada), 2004.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".