Additive Effect on Soybean Peroxidase-Catalyzed Removal of Anilines from Water
Bibliographic record
Abstract
Soybean peroxidase has been shown to be effective in removal of aromatic compounds from wastewater, while the use of additives effectively reduces enzyme concentration requirement, hence overall treatment cost. Enzymatic treatment, an oxidative polymerization, was successful in removal of over 95% of both aniline and o-anisidine. The originality of this study lies in the findings that the additives, sodium dodecyl sulfate (SDS), sodium dodecylbenzenesulfonate (SDBS), Triton X-100, and sodium dodecanoate (SDOD), reduced enzyme concentration requirement, while polyethylene glycol (PEG, average molar mass of 3350 g/mol) had no effect on the required enzyme concentration. In addition, the presence of SDS also enhanced treatment by improving precipitation and color removal. These results are enabling advancement of soybean peroxidase-catalyzed treatment of anilines found in wastewaters as a new sustainable method.
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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) | 0.001 | 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".