A Washing Procedure to Mobilize Mixed Contaminants from Soil
Bibliographic record
Abstract
We conducted a laboratory study to assess the efficiency of nonionic and anionic surfactants in combination with a sparing quantity of ethylenediaminetetraacetate (EDTA) to simultaneously extract heavy metals (HMs) and polychlorinated biphenyl (PCB) compounds from a field-contaminated soil. A soil wash that mobilized both HMs and PCBs was combined with back-extraction with hexane to remove PCBs from the aqueous wash. The aqueous washing suspension was then regenerated by precipitation of the HMs induced by corrosion and hydrolysis of zero-valent Mg to provide a cleaned soil and innocuous extract. Finally, the washing suspension was recycled twice to mobilize more contaminants from the soil particulate fraction. After ultrasonic equilibration, EDTA in admixture with a nonionic surfactant did not appreciably change the efficiency of mobilization of most heavy metals (Al, Cd, Cr, Fe Mn, Ni, and Zn), but did increase the recovery of Cu and Pb. The release of EDTA from HM complexes was efficient for most metals (99%) but was influenced by the chemical characteristics of the surfactant. The EDTA recovery (62-65%) after three cycles of soil washing, hexane back-extraction, and Mg(0) treatment was similar for all reagent combinations. In toto, these studies demonstrate that after treatment with ultrasound, selected heavy metals can be coextracted efficiently from soil with a single washing suspension containing EDTA and a nonionic surfactant.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; both teacher heads agree on what is shown here.
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".