Bioremediation of wastewater containing azo dyes through sequential anaerobic–aerobic bioreactor system and its biodiversity
Bibliographic record
Abstract
Wide range of dyes and dyestuffs used in textile manufacturing are xenobiotic compounds and attract stricter to strict environmental regulations. The ability of microbial consortia to decolorize and metabolize dyes has long been known, and the use of bioremediation based technologies for treating textile wastewater has attracted interest. These dyes are decolorized by microbial consortia but technologies for their complete mineralization are still not developed. The most logical concept for the removal of azo dyes in biological wastewater treatment systems is based on anaerobic treatment, for the reductive decolorization, in combination with aerobic treatment, for the degradation of the by-products (aromatic amines) generated in the anaerobic bioreactor. Several research and review articles were published on anaerobic decolorization; however, research on complete mineralization of dyes through sequential anaerobic–aerobic bioreactors has received greater attention recently. Bioremediation through sequential anaerobic–aerobic bioreactor system has been reviewed in this article with critical appraisal using data generated through our experiments. While reviewing this work, we realized the importance of microbial diversity in a treatment unit to better understand the functional status to enhance the mineralization activity of the bioreactor.
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) | 0.000 | 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 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".