Immobilization of Arsenic from Mining Tailings Using Various Metal Oxides
Bibliographic record
Abstract
Elevated levels of arsenic that leach from mine tailings, sediment and soil can lead to the contamination of surface and groundwater. In this study, various types of metal oxides as immobilizing agents were evaluated. Their effectiveness was determined via leaching tests and selective sequential extraction (SSE) using mine tailings and metal oxides at different weight ratios, reaction times, and types of oxides. Commercial grade metal oxides (MgO, ZnO, Fe3O4, TiO2, CaO and Al2O3) in the form of regular and nanoscale powders were evaluated. Both forms of ZnO (zinc oxide) had a higher capacity to immobilize the arsenic present in the mine tailings than any other oxides tested. Magnetite (Fe3O4) had limited effectiveness whereas all other metal oxides tested had little or no effect. The addition of 7.5% by weight of nanoscale ZnO led to a 99.4% to 99.7% reduction in the amount of arsenic leached from Noranda and Golden Giant mine tailings after 24 h in an acidified water solution at a pH of 3. SSE tests confirmed that ZnO is a very effective immobilizing agent in all five fractions. These results indicate the possibility of developing a remediation process for mining areas as well as other contaminated soils using ZnO.
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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".