Agro-industrial-Produced Laccase for Degradation of Diclofenac and Identification of Transformation Products
Bibliographic record
Abstract
A widely used anti-inflammatory drug, diclofenac (DCF), is recalcitrant in many environmental compartments and poses threat to several aquatic and terrestrial organisms. Enzymatic degradation of emerging contaminants which are often micropollutants, has gained interest for the past few years. However, production of enzymes often incurs high costs. In this study, ligninolytic enzyme laccase was produced by white rot fungi Tremetes versicolor (ATCC 20869) using agro-industrial residues, apple pomace (AP), pulp and paper solid waste (PPSW), and alfa fibers as substrates. Various known inducers for laccase production, such as tween 80 (0.1% ( w /w)), veratryl alcohol (3 mM Kg –1 ), CuSO 4 (3 mM Kg –1 ), and phenol red (3 mM Kg –1 ) were used to enhance laccase production. A maximum laccase activity of 49.16 ± 4.5, 52.4 ± 2.2, and 14.26 ± 0.8 U/gds (units/gram dry substrate) was obtained from apple pomace, PPSW, and alfa plant fibers, respectively, at optimal experimental conditions. Further, the kinetics of the laccase mediated degradation of DCF was studied. At environmentally relevant concentration of DCF (500 μg L –1 ), laccase-catalyzed degradation followed first-order kinetics. At environmentally relevant concentrations pH of 4.5 and temperature of 50 °C was found to be optimal for the effective degradation of DCF with laccase. 3′-Hydroxydiclofenac, 4′-hydroxydiclofenac, and 5-hydroxydiclofenac were identified as the major transformation products during the initial 5 h of degradation. However, after 24 h of degradation, neither DCF nor any transformation products were identified so that the proposed degradation mechanism involved hydroxylation followed by ring opening and final mineralization to CO 2, NH 3, and H 2 O and HCl.
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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) | 0.001 | 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) | 0.001 | 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".