Development of a TOF‐SIMS technology as a predictive tool for the needs of the mineral processing industry
Bibliographic record
Abstract
Abstract Recently, upgrades towards a semiquantitative approach to mineral processing applications using the time of flight (TOF) SIMS (TOF‐SIMS) technique have been developed and implemented. Secondary ion yield at specific instrument parameters for matrix elements in the predominant ore minerals and their comparative normalization factors have been determined. Surface loading quantification for Cu on a variety of ore minerals has shown that signal intensity variability is related to the substrate matrix. Relative sensitivity factors for component loading have been determined and calibration curves for Cu loading on mineral surfaces have established with lower limits of detection in the range of 10 ppm. Given the new semiquantitative approach for surface characterization of minerals, a new test was developed to be used as a predictive tool in mineral flotation separation. The test protocol involved a two‐chamber ball mill where Cu transfer between the pulp and specimen surface was measured by the semiquantitative TOF‐SIMS approach. The test was applied to 13 ores. The reported experimental data on these ores demonstrated the ability of this technique to differentiate Cu transfer over a large dynamic range. The data also demonstrated that the surface loading of Cu on pyrite can be correlated, in some cases, with mineralogy. In others, however, the surface Cu loading observed is not congruent with the mineralogical assessment of the ore sample, but still linked with flotation behavior. This shows that the test could be used with mineralogy to better benchmark a sample before embarking on a flotation flowsheet development programme. Copyright © 2010 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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".