Characterization of the solid leach residues from the iodine-assisted chalcopyrite leaching in ferric sulfate media
Bibliographic record
Abstract
Chalcopyrite dissolution in ferric sulfate media at ambient conditions is slow. The addition of iodide has been found to significantly improve chalcopyrite leaching in the potential range where triiodide (I3−) or diiodine (I2) is the predominant species. In the iodine-assisted chalcopyrite leaching process, elemental sulfur was proposed to be the form of sulfur product while iron precipitation was also observed. Given the potential impact of elemental sulfur and iron precipitates on chalcopyrite leaching, this study analyzed the solid leach residues collected from the iodine-assisted chalcopyrite leaching using different solid characterization techniques. The X-ray diffraction analysis of the bulk residues shows that elemental sulfur was the product of chalcopyrite leaching and that pyrite was unreactive. The cross-sectional analysis by a mineral liberation analyzer (MLA) shows that thick layers of jarosite and elemental sulfur coated the surfaces of the solid particles. Further analysis by X-Ray photoelectron spectroscopy (XPS) with a depth resolution of <10 nm confirmed that elemental sulfur was formed and that the formation of jarosite was favored at 40 and 45 °C. Despite the formation of elemental sulfur and iron precipitates, a near complete dissolution of chalcopyrite could be achieved. Further research is required to understand how the presence of iodine changes chalcopyrite leaching in such a way that surface passivation does not occur.
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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) | 0.001 | 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".