Remediation of a Heavy Metal Contaminated Soil by a Rhamnolipid Foam
Bibliographic record
Abstract
Studies were initiated with a rhamnolipid biosurfactant in columns to simulate in situ conditions. The biosurfactant was injected in the form of a foam and liquid solutions. The study was conducted in three steps: evaluation of the foam characteristics, investigation of pressure buildup by foam injection and removal of metals by the foam. Foam quality of the rhamnolipid was shown to vary between 90 and 99% with stabilities from 17 to 41 minutes. Pressure build up was then evaluated with different flow rates, foam quality and biosurfactant solutions. Metal removal was then evaluated from a sandy soil contaminated with 1,710 ppm of Cd and 2,010 ppm of Ni. Maximum removal was obtained by a foam produced by 0.5% rhamnolipid solution after 20 pore volumes. Removal efficiency for the biosurfactant foam was 73.2 % of Cd and 68.1% of Ni. For the biosurfactant liquid solution, 61.7% Cd and 51.0 % were removed. Distilled water removed only 18 % of both Cd and Ni. Liquid solutions with concentrations of 0.5, 1.0 and 1.5% rhamnolipid at pH values of 6.8, 8 and 10 were also evaluated for their ability to remove metals but they did not show any significant beneficial effects. Therefore, rhamnolipid foam may be an effective and non-toxic method of remediating heavy metal contaminated soils. Further efforts will be required to enable its use at field scale.
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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".