Production of <i>Bacillus amyloliquefaciens</i> OG and its metabolites in renewable media: valorisation for biodiesel production and <i>p</i>-xylene decontamination
Bibliographic record
Abstract
Biosurfactants are important in many areas; however, costs impede large-scale production. This work aimed to develop a global sustainable strategy for the production of biosurfactants by a novel strain of Bacillus amyloliquefaciens. Initially, Bacillus sp. strain 0G was renamed B. amyloliquefaciens subsp. plantarum (syn. Bacillus velezensis) after analysis of the gyrA and gyrB DNA sequences. Growth in modified Landy’s medium produced 3 main recoverable metabolites: surfactin, fengycin, and acetoin, which promote plant growth. Cultivation was studied in the presence of renewable carbon (as glycerol) and nitrogen (as arginine) sources. While diverse kinetics of acetoin production were observed in different media, similar yields (6–8 g·L–1) were obtained after 72 h of growth. Glycerol increased surfactin-specific production, while arginine increased the yields of surfactin and fengycin and increased biomass significantly. The specific production of fengycin increased ∼10 times, possibly due to a connecting pathway involving arginine and ornithine. Adding value to crude extracts and biomass, both were shown to be useful, respectively, for the removal of p-xylene from contaminated water and for biodiesel production, yielding ∼70 mg·g–1 cells and glycerol, which could be recycled in novel media. This is the first study considering circular bioeconomy to lower the production costs of biosurfactants by valorisation of both microbial cells and their primary and secondary metabolites.
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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) | 0.000 | 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".