Immobilization Of Arsenic In Mine Tailings Using Standard And \nNanoscale Metal Oxides
Bibliographic record
Abstract
Elevated levels of arsenic can be found in mine tailings, sediment and soil samples.Leaching of arsenic from tailings can lead to the contamination of surface and groundwater.One potentially sustainable method is to add various agents to stabilize the waste and ensure that arsenic does not leach out of the waste.In the current study, the effectiveness of various types of metal oxides as immobilizing agents was tested.Leaching tests and SSE (Selective Sequential Extraction) were performed on different mixtures of mine tailings and metal oxides, using different weight ratios, reaction times, types of oxides.The mine tailings were taken from different sites in Canada.The metal oxides used were either regular (commercial grade) or nanoscale powders.The additives evaluated were MgO, ZnO, Fe 3 O 4 , TiO 2 , CaO and Al 2 O 3 .These additives were chosen for their successful use as commercial agents in chemical decontamination.The leaching tests were done using a solution of distilled water and sulphuric acid at a pH of 3 to simulate acid rain fall on the mine tailings that could occur.The concentration of arsenic in the leachate was measured using arsenic test kits and ICP-MS instrumentation.It was found that both regular and nanoscale ZnO (zinc oxide) had the highest capacity to immobilize the arsenic present in the mine tailings, whereas the other metal oxides tested and Fe 3 O 4 (magnetite) had little or no effect.Leaching tests performed on Noranda and Golden Giant mine tailings over a 24 hour period revealed that the addition of 7.5% in weight of nanoscale ZnO caused a 99.4% to 99.7% reduction in the amount of arsenic leached into solution of distilled water at a pH of 3. A 91% to 92% reduction was observed when the additives were left to immobilize the tailings for a period of 1 month.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".