Soybean peroxidase‐catalyzed degradation of a sulfonated dye and its azo‐cleavage product
Bibliographic record
Abstract
Abstract BACKGROUND The presence of azo dyes in wastewater from the textile industry is a major environmental concern. The dyes not only make water aesthetically unacceptable, but also have severe toxicological concerns. Research into treatment processes for removal of dyes has focused primarily on decolourization and little attention has been focused on analysis of the degradation products, that could plausibly be more toxic than the parent compound. This study focusses on soybean peroxidase (SBP)‐catalyzed treatment of two azo dyes, Methyl Orange (MO) and CI Direct Yellow 12 (DY12) in water, chosen because they lack phenolic and primary anilino functional groups, which are usually expected to form free radicals under peroxidase catalysis. RESULTS DY12 was found not to be a substrate of SBP, but optimized reaction conditions for 0.50 mmol L–1 MO and 1.0 mmol L–1 p‐anisidine (structurally analogous to 4‐ethoxyaniline, a possible azo‐cleavage product of DY12) achieved ≥95% conversion at exceptionally low minimum effective SBP activities (0.0070 and 0.0018 U mL–1) at pH optima of 4.0 and 5.5 and [hydrogen peroxide]/[substrate] of 2 and 1, respectively. CONCLUSIONS Mass spectrometric (MS) analysis for the substrates revealed formation of dimers and trimers for p‐anisidine. For MO, high‐performance liquid chromatography, UV‐visible spectrophotometry and MS provided evidence of azo‐bond cleavage, hetero‐coupling of the dye with the cleavage product and also self‐coupling of the dye through tertiary amine activation. © 2020 Society of Chemical Industry (SCI)
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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".