Laccase-Catalyzed Oxidation of Mixed Aqueous Phenolic Substrates at Low Concentrations
Bibliographic record
Abstract
It has been proposed that Trametes versicolor laccase can be used to detoxify wastewaters that are contaminated with phenolic pollutants. However, the oxidation of phenols at low concentrations may be impacted if other substrates tend to interfere with or enhance the oxidation of the target substrate. To test this, experiments were conducted to evaluate effects arising from the simultaneous presence of mixed substrates including phenol (P), estradiol (E2), cumylphenol (CP), and triclosan (TCL), each of which are characterized by different rates of oxidation and tendencies to inactivate laccase. Slower and faster substrates were found to have only minor negative impacts upon the rate of conversion of targeted substrates, except where they tended to cause inactivation. No enhancements in substrate oxidation were observed. A multi-substrate kinetic model was shown to be able to accurately predict the time course of reactions of mixed substrates over extended periods at micromolar and sub-micromolar concentrations, except when estradiol and triclosan were simultaneously present. In this case, more enzyme inactivation was observed than would be expected from the oxidation of individual substrates alone. The utility of the model for providing insights into the reaction phenomenon and for evaluating the feasibility of oxidizing targeted substrates in the presence of other substrates is demonstrated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".