Laccase-Driven Transformation of High Priority Pesticides Without Redox Mediators: Towards Bioremediation of Contaminated Wastewaters
Bibliographic record
Abstract
In this study, Pleurotus dryinus was grown on municipal biosolids (BS) as the substrate to produce laccase for the removal of pesticides (fungicides, herbicides, and insecticides) from wastewater. Among the various types of BS tested, sterilized biosolids were the most promising substrate for laccase production by P. dryinus with a maximal laccase activity (162.1 ± 21.1 U/g dry substrate), followed by hygenized biosolids (96.7 ± 17.6 U/g dry substrate), unsterilized biosolids (UBS) (31.9 ± 1.2 U/g dry substrate), and alkali-treated biosolids (8.2 ± 0.4 U/g dry substrate). The ultrasound-assisted extraction of this enzyme from fermented UBS was carried out with 0.1 M phosphate buffer at pH 7.0, which increased the enzyme activity of the crude extract by 30%. To test the catalytic potential of the biocatalyst in real matrices, 1 U/ml of recovered crude laccase extract was applied for 24 h for the removal of 29 pesticides (nine fungicides, 10 herbicides, and 10 insecticides) either separately or as a mixture from spiked biologically treated wastewater effluent. When treated with crude enzyme extract, high-priority herbicides metolachlor and atrazine were completely removed, while 93%–97% of the insecticides aldicarb, spinosad, and azinphos-methyl and up to 91% of kresoxim-methyl were removed. Promising results were obtained with BS-derived crude enzyme extract exhibiting improved pesticides removal, which may be due to the mediator effect resulting from the catalytic transformation of other molecules in the cocktail. The results demonstrated a promising integrated bioprocess for the removal of pesticides in wastewater using crude laccase obtained from BS.
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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) | 0.000 | 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".