Awaruite, a new large nickel resource: Activation by ammonium sulfate and thiosulfate for flotation
Bibliographic record
Abstract
Awaruite is a native nickel-iron alloy with high nickel content and mainly present in serpentinized ultramafic rocks. Recent discoveries have demonstrated the potential for awaruite to contribute to the economics of a nickel deposit. Awaruite selectively floats in weakly acidic conditions with xanthate as collector. However high reagent dosages are required in such conditions since xanthate decomposes and ultramafic rocks are acid consumers. In this work, a novel reagent scheme including ammonium sulfate and sodium thiosulfate is proposed to float awaruite in neutral conditions from ultramafic rocks. Electrochemical studies were carried out on awaruite samples to demonstrate the effect of low concentrations of these reagents on the awaruite surface. The awaruite passivation layer formed in alkaline conditions (natural slurry pH) can, at least, be partially dissolved in the presence of low concentrations of ammonium sulfate and thiosulfate in neutral conditions. After the passivation layer is partially removed, the xanthate collector reacts with the awaruite surface and induces hydrophobicity, thus enabling the awaruite flotation. Microflotation and bench scale flotation tests demonstrate the applicability of the reagent scheme herein proposed. This reagent scheme allows the flotation of awaruite in conditions where xanthate is stable and reduces the acid addition required to adjust the pH.
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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 | 0.001 | 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".