The optimization of reactive black 5 dye removal using Coprinus cinereus peroxidase (CIP)
Bibliographic record
Abstract
Coprinus cinereus (NBRC 30628) peroxidase was implemented to eliminate the diazo dye of reactive black 5 (RB5). The optimization was conducted in batch mode using three approaches, i.e., the one-factor-at-a-time (OFAT), factorial design, and response surface methodology (RSM). Based on the results of the OFAT method, the optimum conditions for decolorization of RB5 dye were at a temperature of 30oC, a pH of 9-10, an H2O2 concentration of 3.9 mM, and an RB5 concentration of 40 mg/L. In the first stage of statistical optimization, these factors plus enzyme activity were screened by the 2-factorial design, wherein enzyme activity, temperature, and hydrogen peroxide concentration were distinguished as the most significant parameters in the enzymatic decolorization of RB5. In the second stage, RSM was applied over three adopted factors through the central composite design (CCD), and a reduced cubic polynomial model was generated, which indicated an accurate regression (R2 = 0.997, Adj.R2 = 0.994) and no significant lack of fit (p-value> 0.05). The contour and surface plots suggested that the removal efficiency was enhanced by increased enzyme activity and decreased H2O2 concentration and temperature. The optimum condition was obtained at 1.0 mM H2O2, 6 U/mL enzyme activity, and 35oC for a maximum decolorization efficiency of 96.046%.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".