Influence of the medium conditions on enzymatic oxidation of bisphenol A
Bibliographic record
Abstract
Abstract A multistep response surface methodology (RSM) was successfully applied to optimise the medium conditions for the enzymatic polymerisation of bisphenol A (BPA). The laccase enzyme used as the catalyst was derived from Funalia trogii (ATCC 200800) yeast culture. The enzymatic polymerisation rate of BPA, based on the measurements of the initial dissolved oxygen (DO) consumption rate in a closed batch system, was studied through RSM. Initially the most effective medium factors, which are monomer concentration (mg/L), temperature (°C) and solvent content (% methanol), were determined through Plackett–Burman Design (PBD), then the steepest ascent combined with central composite design (CCD) steps were applied to evaluate the optimal reaction conditions for the enzymatic polymerisation. The optimal conditions were evaluated to be 748.46 mg/L, 32.24°C and 15.92% for monomer concentration, temperature and solvent content, respectively. A quadratic model was developed through RSM to represent DO consumption in the medium. The maximum DO consumption rate was calculated to be 0.093 mg DO/L min. Several repetitions were conducted at the optimal conditions to validate the system performance. The data evaluated from the quadratic model were in good agreement with those measured experimentally. The variations between the values did not exceed 10%. The correlation coefficient, R2, was calculated to be 0.95, which indicates that 95% of results can be explained by model.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".