Decolorization of textile azo dye Novacron Red using bacterial monoculture and consortium: Response surface methodology optimization
Bibliographic record
Abstract
The present study was intended toward the optimization of a textile dye Novacron Red decolorization by single and mixed culture of Bacillus strains namely, B. firmus, B. filamentosus and B. subterraneus. Optimization of dye decolorization using Bacillus monocultures was conducted using central composite design. The maximum dye decolorization achieved under optimized conditions for B. firmus, B. filamentosus and B. subterraneus was 89.24%, 88.28% and 88.45%, respectively. The effect of various consortia of selected Bacillus strains on dye removal was evaluated by applying a mixture design. The best dye (100 mg/L) decolorization yield (84%) was achieved using the consortium of B. filamentosus and B. subetrraneus.The Fourier Transform Infrared Spectroscopy analyses confirmed biodegradation potential of the two Bacillus strains. The results highlighted the potential of mono- and co-cultures of Bacillus strains for application in textile wastewater treatment. PRACTITIONER POINTS: Novel dye-decolorizing Bacillus strains were isolated from marine sediment. Optimization of decolorization was conducted using response surface methodology. Efficient decolorization of textile dye by Bacillus strains on mono- and co-cultures. The efficiency of the consortium B. filamentosus and B. subetrraneus on dye removal.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".