Fly ash based robust biocatalyst generation: a sustainable strategy towards enhanced green biosurfactant production and waste utilization
Bibliographic record
Abstract
N3-1P attached onto the surface of fly ash acted as a robust and effective biocatalyst. Zeta potential analysis and scanning electron microscope (SEM) characterization were conducted to help unravel the biocatalyst formation. High-value biosurfactant products were then produced in an efficient and sustainable manner. Stimulation by a fly ash assisted biocatalyst on biosurfactant production was confirmed. The biosurfactant yield was boosted over ten times after 24 hours, at a fly ash dosage of 0.5%. The highest biosurfactant yield was achieved after five days, with a final productivity of 305 critical micelle dilution. The underlying mechanism of fly ash assisted biosurfactant production was tracked through it exerting an effect on the quorum sensing system. Fourier-transform infrared (FTIR) spectroscopy and matrix assisted laser desorption/ionization-time of flight (MALDI-TOF) analysis demonstrated that the final biosurfactant product belonged to the lipopeptides. This research output is expected to accelerate the development of more effective bioreactors, and make a continuous contribution to high-value product generation and waste reduction.
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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 | 0.001 | 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".