Generation of biosurfactants by <i>P. aeruginosa gi</i> | <scp>KP163922</scp> | on waste engine oil in a free and immobilized cells system
Bibliographic record
Abstract
Abstract This study investigated biosurfactant production by the bacterial strain of P. aeruginosa gi |KP 163922| for a free and immobilized cells system using waste engine oil (WEO) as a substrate. The polyurethane foam (PUF) cubes (1 cm × 1 cm × 1 cm) were used as carriers for the immobilization. The batch experiments were performed in Erlenmeyer flasks and monitored at every 24‐h interval for both cell systems. The microbial population was counted using the plate count method, and the hydrocarbon degradation percentage was calculated to evaluate the bacterial activity. Surface tension was measured at regular intervals to ensure the presence of biosurfactants. The maximum reduction was 37 and 35 mN/m in a free and immobilized cells system. Immobilization of cells using PUF was found to be efficient in supporting bacterial growth, and after 48 h of incubation, the growth was 2.5 (±0.58) × 10 11 CFU/mL. The chemical characterization using Fourier transform infrared (FTIR) spectroscopy confirmed the obtained product as rhamnolipid. Crude biosurfactant yield was found to be maximum in the case of the immobilized system, which was approximately 18 g/L. Scanning electron micrographs (SEM) of the used PUF cubes showed the strong adherence of biofilm to the cube surface and the potential of its reuse in multiple cycles. Gas chromatography–mass spectrometry (GC–MS) analysis confirms that the immobilized strain of P. aeruginosa exhibited superior biodegradation capabilities compared to free cells. Specifically, it was capable of reducing the concentration of polyaromatic hydrocarbons and converting more significant aliphatic compounds into metabolic byproducts such as alkanes, alkenes, cycloalkanes, and carbonyl groups.
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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) | 0.001 | 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) | 0.001 | 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".