Fish Waste Based Lipopeptide Production and the Potential Application as a Bio-Dispersant for Oil Spill Control
Bibliographic record
Abstract
There is a growing acceptance worldwide for the application of dispersants as an offshore oil spill response. The development of dispersants with less toxicity and higher biodegradability is a step forward to implementing more environmentally friendly oil spill response techniques. Biosurfactant based environmental biotechnology development has acquired increasingly attention. A bio-dispersant agent with a lipopeptide biosurfactant produced by Bacillus substilis N3-1P as the key component was formulated in this study. The economic feasibility of biosurfactant production using fish waste-based peptone as a nutrient substrate was evaluated. Protein hydrolysate was prepared from cod liver and head wastes. Hydrolysis conditions (i.e., time, temperature, pH and enzyme to substrate level) for preparing protein hydrolysates were optimized by response surface methodology using a factorial design. The fish liver and head waste generated peptones improved biosurfactant productivities to 54.72 critical micellar dilution (CMD) and 47.59 CMD, respectively. Biosurfactants generated by fish liver peptone had a lower critical micellar concentration of 0.18 g L-1 and could reduce the surface tension of distilled water to 27.9 mN/m. Structure characterization proved that the generated biosurfactants belong to lipopeptide. As a replacement to the key surfactant dioctyl sulfosuccinate sodium (DOSS) used in Corexit 9500, a binary mixture of lipopeptides and DOSS was developed. Lipopeptides exerted synergistic effects on the binary surfactant system. The highest dispersion efficiency (76.8% for Alaska North Slope oil) was achieved at a biodispersant composition of 80/20 (V/V) of lipopeptides/DOSS. The experiment result developed an environmentally friendly, cost-efficient biodispersant that could be applied for marine oil spill treatment. This study also leads to an effective waste management option to produce high-added value bioproducts.
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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) | 0.000 | 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".