Soybean Peroxidase Catalyzed Decoloration of Acid Azo Dyes
Bibliographic record
Abstract
BACKGROUND: Some industrial manufacturing processes generate and release dyes as water pollutants, many of which are toxic and hazardous materials. There is a need for milder, greener methods for dye treatment. OBJECTIVES: The objective of the present study was to investigate and optimize azo dye decoloration by a crude soybean peroxidase (SBP), based on two dyes that have widespread industrial use, but that differ greatly in structural complexity, Acid Black 2 and Acid Orange 7, and to investigate the effects of specific parameters on the removal process. METHODS: Batch reactors were used to remove 95% of the dyes' color and to produce substantial precipitates. RESULTS: The optimum pH for enzymatic decoloration of Acid Black 2 was in the acidic region, pH 4.4, and that of Acid Orange 7 occurred under neutral conditions, pH 6.9. The minimum enzyme activity needed for sufficient removal was 1.2 U/mL for both dyes at 0.5 mM. The minimum molar hydrogen peroxide/substrate ratio was 3 for Acid Orange 7 and 2.5 for Acid Black 2 to achieve approximately 95% removal. First-order fitting of progress curve data collected under the respective optimum conditions gave half-lives of 23.9 and 28.9 minutes for Acid Orange 7 and Acid Black 2, respectively. CONCLUSIONS: The feasibility of SBP-catalyzed treatment of industrial dyes Acid Black 2 and/or Acid Orange 7, or dyes that resemble them, as they might occur in industrial effluents, was successfully demonstrated. COMPETING INTERESTS: The authors declare no competing financial interests.
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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".