Soybean Peroxidase-Catalyzed Oxidative Polymerization of Phenols in Coal-Tar Wastewater: Comparison of Additives
Bibliographic record
Abstract
Soybean peroxidase (SBP), as a crude extract from soybean seed coats, was applied to remove 15 mM of phenols from coal-tar wastewater. The enzyme required for the conversion of phenols in coal-tar wastewater was less than that predicted by studies with synthetic wastewater. Step additions of both SBP and hydrogen peroxide reduced the SBP concentration requirement for >95% conversion of phenol. Polyethylene glycol (PEG) showed no improvement on the conversion efficiency, whereas sodium dodecyl sulfate (SDS) showed significant improvement that was better than step addition. Corroborative studies with synthetic wastewater have shown that Triton X-100 enabled the lowest SBP concentration for 95% conversion of 1.0 mM phenol followed by SDS, sodium dodecylbenzenesulfonate, and then PEG. Most significantly, evidence suggests that the anionic surfactants, SDS and sodium dodecylbenzenesulfonate, do not work in the same way as nonionic Triton X-100 or as PEG. Aluminum hydroxide gel (alum) was investigated for removal of polymeric colored products and surfactants after enzymatic reaction. The originality of this work lies, first, in the application of SBP to real industrial wastewater, with its catalytic lifetime extended by the presence of surfactant, and second, the picture that is emerging from the differences in mechanism by which various surfactant types and PEG effect such an enhancement. The impact of these new insights with surfactants is to enable SBP-based treatment to reach cost-effectiveness for industrial streams of the type studied here.
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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 | 0.000 | 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 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".