Recycling of complexometric extractants to remediate a soil contaminated with heavy metals
Bibliographic record
Abstract
Equilibrations were performed with complexing reagent(s) to mobilise Cd, Cu, Mn, Ni, Pb and Zn from a contaminated urban soil. The metal-laden aqueous extract was treated with zero-valent magnesium (Mg0) or bimetallic mixture (Pd0/Mg0 or Ag0/Mg0) to precipitate the heavy metals from solution while liberating the chelating reagent(s). Post precipitation, the pH of aqueous supernatant fraction was readjusted to approximately 5 and the solution was re-combined with the soil particulates to extract more heavy metal pollutants. A sparing quantity of EDTA (10 mmoles) mobilised 32-54% of the 5 mmoles of heavy-metals from the soil with three cycles but only 0.1% of the iron was removed. Three successive extractions with a mixture of complexing reagents (3 mmoles), 1:1 EDTA plus HEDC [bis-(2-hydroxyethyl)-dithiocarbamate], mobilised approximately 49% of the Pb, approximately 18% of the Zn and approximately 19% of the Mn burden but only 7% of the Cu, and 1% of the Fe from this soil. An appreciable fraction of the mobilised Pb and Cu and a portion of the Zn was cemented to the surfaces of the excess magnesium whereas virtually all of the Fe and Mn was removed from solution as insoluble hydroxides.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".