Isolation and Characterization of Biosurfactant-and Bioemulsifier-Producing Bacteria from Petroleum Contaminated Sites in Western Canada
Bibliographic record
Abstract
Biosurfactant-producing bacteria were isolated from two petroleum contaminated sites in western Canada. Seven potential biosurfactant/bioemulsifier-producing isolates were screened and characterized. All of the seven isolates were able to form emulsions. Emulsion-stabilizing capacity was also measured up to 48 hrs. Strain C-111-2 and C-203-2 would lead to highly reduced surface tension. For strain C-203-2, the optimum conditions that supported bacteria growth and production were investigated. The influences of carbon sources, medium pH values, and temperature were taken into account. The experimental results indicated that the crude oil and glucose were promising carbon sources for biosurfactants production; the isolated strains produced a maximum concentration of biosurfactant in a neutral pH environment and showed a higher surface activity under the temperature level of 35°C than that under 10°C. To further optimize the carbon and nitrogen source for biosurfactant production, response surface methodology (RSM) was applied to explore the favorable concentration of two carbon sources: glucose, crude oil, and one nitrogen source, NaNO3. The optimal concentration of 8.1g/L, 4% and 3.9 g/L for glucose, crude oil, and NaNO3, respectively, which can be obtained through RSM analysis.
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 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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".