Emerging contaminants bioremediation by enzyme and nanozyme-based processes – A review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it