Decontamination of metals, pentachlorophenol, and polychlorined dibenzo-<i>p</i>-dioxins and dibenzofurans polluted soil in alkaline conditions using an amphoteric biosurfactant
Bibliographic record
Abstract
In this paper, flotation in acidic conditions and alkaline leaching soil washing processes were compared to decontaminate four soils with variable contamination with metals, pentachlorophenol (PCP), and polychlorodibenzo dioxins and furans (PCDD/F). The measured concentrations of the four soils prior treatment were between 50 and 250 mg/kg for As, 35 and 220mg/kg for Cr, 80 and 350mg/kg for Cu, and 2.5 and 30mg/kg for PCP. PCDD/F concentrations reached 1394, 1375, 3730, and 6289ng/kg for F1, S1, S2, and S3 soils, respectively. The tests were carried out with masses of 100g of soil (fraction 0-2 mm) in a 2 L beaker or in a 1 L flotation cell. Soil flotation in sulphuric acid for 1 h at 60 degreeC with three flotation cycles using the surfactant cocamidopropyl betaine (BW) at 1% allows the solubilization of metals and PCP with average removal yields of 85%, 51%, 90%, and 62% for As, Cr, Cu, and PCP, respectively. The alkaline leaching for 2 h at 80 degreeC solubilizes As, Cr, Cu, and PCP with average removal yields of 60%, 32%, 77%, and 87%, respectively. Tests on PCDD/F solubilization with different surfactants were carried out in combination with the alkaline leaching process. PCDD/F removal yields of 25%, 72%, 70%, and 74% for F1, S1, S2, and S3 soils, respectively, were obtained using the optimized conditions.
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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".