Biosurfactant produced by a Rhodococcus erythropolis mutant as an oil spill response agent
Bibliographic record
Abstract
Biosurfactants have been considered as superior alternatives to currently used surfactants as they are generally more biodegradable, less toxic, and better at enhancing biodegradation. However, the application of biosurfactants is limited by the availability of economic biosurfactants and the corresponding producers that can work effectively. Hyperproducers generated by metabolic engineering of biosurfactant producers are highly desired to overcome this obstacle. A Rhodococcus erythropolis SB-1A strain was isolated from offshore oily water samples. One of its mutants derived by random mutagenesis with ultraviolet radiation, producing high levels of biosurfactants, was selected by the oil spreading technique. The mutant produces biosurfactants with critical micelle dilutions approximately four times those of the parent strain. The results obtained with thin layer chromatography indicated the produced biosurfactant remained unchanged between the mutant and the parent strain. In addition, the produced biosurfactants were recovered with solvent extraction and applied as the oil spill response agents. Based on the baffled flask test (BFT) results, the dispersion efficiency of the biosurfactants produced by the mutant is higher than that induced by the parent strain. When compared with Corexit dispersants, it was found that the produced biosurfactants performed better than Corexit 9527 and were comparable with Corexit 9500.
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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 | 0.000 | 0.001 |
| 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".