A ToF‐SIMS investigation on correlation between grinding environments and sphalerite surface chemistry: implications for mineral selectivity in flotation
Bibliographic record
Abstract
Abstract Changes in mineral surface properties during grinding play a key role in flotation performance. Time of flight secondary mass spectrometry (ToF‐SIMS) surface chemical analytical studies have shown that flotation separation of sphalerite from chalcopyrite is significantly affected by the oxidation of metal species on the surface of sphalerite. The intensity of iron oxyhydroxyl species on the surface of sphalerite has a positive correlation with poor recovery of sphalerite. Given the link between the presence of oxide species on the surface of sphalerite and a lower recovery during copper flotation, a laboratory study was initiated to evaluate the potential for sphalerite surface oxidation control and improving recovery through grinding. For the investigation, a ball mill that allowed for monitoring pulp chemistry during grinding was utilized to study the impact of grinding conditions on selective flotation of sphalerite. ToF‐SIMS was used to identify and measure the variability in sphalerite surface species as a result of the different test parameters. Variable mill parameters include two types of grinding media, aeration conditions, addition of FeSO 4 , and altering the pyrite content in the feed ore. ToF‐SIMS analyses of mill discharge samples identified higher intensities of iron oxyhydroxyl species on sphalerite surface subsequent to grinding with mild steel balls, in condition of aeration, use of FeSO 4 , and by increasing the pyrite content of the feed ore. The higher adsorption of iron oxyhydroxyl species on the surface of sphalerite should be consistent with the lower recovery. To verify this, bench‐scale flotation tests in the presence and absence of FeSO 4 were performed; results correlated iron oxyhydroxyl species with the poor sphalerite recovery. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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".