Stimulating in‐soil rhamnolipid production in a bioslurry reactor by limiting nitrogen
Bibliographic record
Abstract
A soil with aged contamination from lubricating oil (LO) and polychlorinated biphenyls (PCBs) was treated in a bioslurry reactor to investigate in-soil biosurfactant production by Pseudomonas aeruginosa, the most abundant indigenous, culturable, hydrocarbon-degrading microorganism. After 2 days of growth on LO, a depletion of nitrogen stimulated the production and accumulation of rhamnolipids to levels roughly 20 times the critical micelle concentration. Surface tensions and concentrations of monorhamnolipid and dirhamnolipid, PCBs, and total petroleum hydrocarbons (TPH) were measured in a slurry filtrate. Soil-bound PCBs and TPH were also quantified. Rhamnolipid production was observed within 1 to 2 days after nitrogen depletion in each of the 10 batches tested. By day 6, total rhamnolipid concentrations increased from below detection to average values over 1,000 mg/L, which caused over 98% of soil-bound PCBs and over 99% of TPH to be emulsified and recovered in the filtrate. After 70 days, rhamnolipid concentrations were only reduced by 15%, because of nitrogen-limited rates of rhamnolipid biodegradation. The results show that in-soil biosurfactant production can be stimulated in a controlled way with nutrient limitation and can be used to achieve soil washing.
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) | 0.001 | 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".