Comparison of soybean peroxidase with laccase in the removal of phenol from synthetic and refinery wastewater samples
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Bibliographic record
Abstract
Abstract BACKGROUND: Several studies have demonstrated the feasibility of treating aqueous phenols and aromatic amines with oxidoreductases in synthetic wastewater samples. However, little work has been done on the effectiveness of enzymatic treatment on real wastewater. Here a comparison was made of the oxidative coupling of phenol catalyzed by laccase or soybean peroxidase (SBP) using synthetic and refinery wastewaters. RESULTS: Optimization of pH, enzyme concentration, effect of polyethylene glycol (PEG) addition, and reducing anions were examined for a 3 h reaction time. Laccase had an optimum pH of 5.6–6.0, while SBP had a broad optimum from 6.0 to 8.0. In synthetic samples in tap water to achieve ≥ 95% removal of 1.0 mmol L −1 phenol in 3 h required 0.12 and 1.5 U mL −1 of catalytic activity of laccase and SBP, respectively. In refinery samples comparable removals required 1.2‐ to 1.8‐fold more enzyme than in synthetic tap water samples. Added PEG allowed for a small reduction in the SBP concentration for synthetic wastewater but was ineffective with either enzyme in treating refinery samples. Reducing ions increased the demand for oxidant but, with the exception of cyanide, phenol removal still occurred. CONCLUSION: Both laccase and SBP were effective in removing phenol from aqueous refinery samples, albeit at slightly higher concentrations than required for the corresponding synthetic samples. Copyright © 2008 Society of Chemical Industry
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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