Removal of benzene from wastewater via Fenton pre-treatment followed by enzyme catalyzed polymerization
Bibliographic record
Abstract
This study investigated the feasibility of a two-step process for the removal of benzene from buffered synthetic wastewater. Benzene is outside the scope of enzymatic removal. In order to remove it from wastewater using enzyme, its pretreatment by modified Fenton reaction was employed to generate the corresponding phenolic compounds. In the first phase, the optimum pH, H2O2 and Fe2+ concentrations and reaction time for the Fenton reaction were determined to maximize the conversion of benzene to phenolic compounds without causing significant mineralization. The pretreatment process was followed by oxidative polymerization of the phenolic compounds catalyzed by a laccase from Trametes villosa. Factors of interest for the three-hour enzymatic treatment were pH and laccase concentration. Under optimum Fenton reaction conditions, 80% conversion of the initial benzene concentration was achieved, giving a mixture containing oxidative dimerization product (biphenyl) and hydroxylation products (phenol, catechol, resorcinol, benzoquinone and hydroquinone). Enzymatic removal of biphenyl and benzoquinone was not possible but 2.5 U/mL laccase was successful in removal of the rest of the phenolic
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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".