Enzyme-catalysed oxidation of 17beta-estradol using immobolized laccase from «T. Versicolor»
Bibliographic record
Abstract
Endocrine disruption is a problem of increasing environmental significance, as anomalies continue to be discovered in wildlife exposed to a variety of exogenous toxic compounds released into the aquatic environment through municipal and industrial effluents and agricultural runoff. The estrogens excreted by humans and entering aquatic systems via sewage treatment plants are of particular interest, as estrogen excretion cannot be feasibly controlled at the source and estrogens are among the most potent endocrine disruptors known. As phenolic compounds, estrogens are amenable to oxidation through the catalytic action of oxidative enzymes. Earlier work was directed toward characterizing the removal of estrogens using peroxidase enzymes as well as the fungal laccase Trametes versicolor in batch reactions. The ability of this laccase enzyme has been studied extensively and has demonstrated a very good ability to remove substrates such as phenol, bisphenol A and 17-beta estradiol (E2) from aqueous solutions. In order to minimize the amount of enzyme required to achieve effective treatment, this study focuses on characterizing the removal of E2 using immobilized laccase. Through this approach, it is anticipated that treatment costs will be reduced since immobilization permits the re-use of the active enzyme, rather than discarding the enzyme with treated solutions. The enzyme was immobilized by covalent bonding onto silica beads and the reactions were conducted in a bench-scale continuous-flow packed bed reactor. The influent concentration of E2 was 10 µM for most studies. The effects of mean residence time were determined for several influent E2 doses, and observable E2 transformation occurred under the reaction conditions employed. The stability and reactivity of the immobilized enzyme were observed over varying temperature and pH. As expected, conversion declined when the temperature of the system was changed from room temperature to near freezing at pH 5.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".