Biosurfactant production by native marine bacteria ( <scp> <i>Acinetobacter calcoaceticus</i> </scp> <scp>P1‐1A</scp> ) using waste carbon sources: Impact of process conditions
Bibliographic record
Abstract
Abstract The high cost of biosurfactant production is an obstacle for widespread commercial applications. Cost‐effective generation of biosurfactants could be achieved using industrial wastes and by‐products as substrates and tailoring cultural conditions. In this work, waste streams including refined waste cooking oil and crude glycerol were compared to each other and to commercial carbon sources. Based on this assessment, the waste cooking oil was selected for further studies. A response surface methodology (RSM) was then used to study biosurfactant production by Acinetobacter calcoaceticus P1‐1A strain (a strain indigenous to the North Atlantic Ocean) using the refined waste cooking oil as the sole carbon source. The concentrations of carbon, nitrogen, and NaCl, as well as the initial pH and temperature were varied. The emulsification index was measured as the response. The cultural conditions to reach the maximum emulsification index (68.17%) were 0.0435 v/v (4.35 vol.%) refined waste cooking oil, 6.5 g/L ammonium sulphate, 13.5 g/L NaCl, initial pH of 7.7, and temperature of 34.8°C. The experimental validation of the predicted response under optimum conditions was performed with 862 mg/L of the biosurfactant product generated. The product showed high thermal, pH, and salinity stability. The use of this indigenous bacteria combined with the use of a no‐cost carbon source from waste has the potential to not only reduce costs associated with biosurfactant production but also to produce a biosurfactant better suited to treat oil spills in the harsh environment of the North Atlantic and other cold waters.
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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 | 0.000 |
| 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".