Response Surface Methodology for Optimization of Enzyme-Catalyzed Azo Dye Decolorization
Bibliographic record
Abstract
Azo dyes are a water-pollution problem causing damage to ecosystems and human health. Soybean peroxidase–catalyzed reactions of azo dyes, Acid blue 113 (AB113) and Direct black 38 (DB38), were optimized for color removal using response surface methodology on a Box–Behnken design (BBD). Parameters optimized were H2O2 concentration (mM), pH, and enzyme concentration (U/mL; U is a standard unit of catalytic activity). Optimum conditions for AB113 were pH 4.49, 2.57 mM H2O2, and 1.52 U/mL of enzyme for a predicted 5.6% color remaining (experimental value of 8.1%) and R2 value of 99.68%; and for DB38 the conditions were pH 3.68, 2.92 mM H2O2, and 2.84 U/mL of enzyme for a predicted 3.6% color remaining (experimental value of 5.1%) and R2 value of 99.07%. In addition, the agreement with the one-factor-at-a-time approach was checked. The BBD is a less time-consuming approach that allows identification of interactions between parameters. Kinetic studies (Michaelis–Menten model) quantitatively confirmed the efficiency and effectiveness of enzymatic dye treatment.
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How this classification was reachedexpand
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 | 0.001 | 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 teacher head, 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".