Influence of Rhamnolipids and Triton X-100 on the Biodegradation of Three Pesticides in Aqueous Phase and Soil Slurries
Bibliographic record
Abstract
The effect of surfactants on the biodegradation of trifluralin and atrazine (by Streptomyces PS1/5) and coumaphos (by degrading consortia from a contaminated cattle dip) in liquid cultures and soil slurries was tested at different concentrations of a rhamnolipid mixture (Rh-mix) and Triton X-100 (TX-100). The extent of trifluralin biodegradation in liquid culture was improved at high concentrations of both surfactants. The extent of atrazine degradation dropped in the presence of either surfactant. Coumaphos biodegradation improved slightly at Rh-mix dosages >3000 microM; however, it was readily inhibited by TX-100 at amounts above the critical micelle concentration. In soil slurries, the extent of both trifluralin and atrazine biodegradation was higher in Hagerstown A (HTA) soil than in Hagerstown B (HTB) soil and was not significantly affected by the presence of either surfactant. The onset of trifluralin biodegradation was retarded at higher concentrations of surfactants. In the absence of surfactant, up to 98% of coumaphos in both soil slurries was transformed. At increasing dosages of Rh-mix, the onset of coumaphos biodegradation was retarded, but the removal efficiency of the pesticide increased. Rh-mix and TX-100 depletion was observed during Streptomyces PS1/5 growth in liquid cultures. Rh-mix concentration also decreased during coumaphos biodegradation, whereas TX-100 concentration was not affected. These results suggest that surfactants, added for the purpose of increasing the apparent water solubility of hydrophobic organic compounds, may have unintended effects on both the rate and extent of biodegradation of the target compounds if the surfactants can also be degraded by the microorganisms in the system.
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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 | 0.001 |
| 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".