Approaches to soil remediation by complexometric extraction of metal contaminants with regeneration of reagents
Bibliographic record
Abstract
Complexometric equilibrations were performed with six chelating reagents to mobilise Cu, Mn, Pb and Zn from a contaminated urban soil. The metal-laden aqueous extract was treated with sodium diethyldithiocarbamate (DEDTC) to precipitate the heavy metals from solution while liberating the chelating reagent. The aqueous supernatant fraction was then re-combined with the soil particulates to extract more pollutants. A sparing quantity of EDTA (ethylenediaminetetraacetic acid; 10 mmol) mobilised 32-54% of the 5 mmol of heavy metals from the soil with three cycles but only 0.1 and 1.0% of the iron and magnesium, respectively, was removed. Whereas DPTA (1,3-diamino-2-hydroxypropane-N,N,N',N'-tetraacetic acid) and citric acid also mobilised each of the heavy metals to some extent and continued to extract these metals during all three cycles, the DTPA (diethylenetriamine pentaacetic acid), although efficient initially, could not be recycled with these conditions. ADA [N-(2-acetamido)iminodiacetate] and SCMC [(S)-carboxymethyl-L-cysteine] were selective for copper and zinc but mobilised only Cu when recycled. An alternate means of regenerating the chelating reagent involved treatment of the aqueous extract with magnesium (Mg0) granules. Excess HEDC [bis(2-hydroxyethyl)dithiocarbamate] mobilised appreciable quantities (19-57%) of heavy metals from the soil and retained its complexing activity when recycled. An appreciable fraction of the mobilised Pb and Cu and a portion of the Zn were cemented to the surfaces of the excess magnesium whereas virtually all of the Fe and Mn was removed from solution as insoluble hydroxides.
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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 | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".