Emerging contaminants bioremediation by enzyme and nanozyme-based processes – A review
Bibliographic record
Abstract
Due to their widespread occurrence and the inadequate removal efficiencies by conventional wastewater treatment plants, emerging contaminants (ECs) have recently become an issue of great concern. Current ongoing studies have focused on different physical, chemical, and biological methods as strategies to avoid exposing ecosystems to significant long-term risks. Among the different proposed technologies, the enzyme-based processes rise as green biocatalysts with higher efficiency yields and lower generation of toxic by-products. Oxidoreductases and hydrolases are among the most prominent enzymes applied for bioremediation processes. The present work overviews the state of the art of recent advances in enzymatic processes during wastewater treatment of EC, focusing on recent innovations in terms of applied immobilization techniques, genetic engineering tools, and the advent of nanozymes. Future trends in the enzymes immobilization techniques for EC removal were highlighted. Research gaps and recommendations on methods and utility of enzymatic treatment incorporation in conventional wastewater treatment plants were also discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".