Soybean Peroxidase Catalyzed Decoloration of Acid Azo Dyes
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it