Study on the Interaction between Galena and Sphalerite During Grinding Based on the Migration of Surface Components
Bibliographic record
Abstract
In Pb-Zn ore flotation, unintentional activation of sphalerite often leads to difficult separation of Pb and Zn minerals, during which grinding plays a key role in unintentional activation. Therefore, the aim of this study was to evaluate the surface component changes of two different mineral particles and to propose the interaction between galena and sphalerite during mixed grinding using time-of-flight secondary ion mass spectrometry (ToF-SIMS). The results show that after mixed grinding of the galena and sphalerite, the Pb content on the sphalerite surface increased with the decrease of Zn and Fe contents on the sphalerite surface. The lead ions from galena were obviously absorbed onto the sphalerite surface, while the zinc and iron ions from sphalerite were not obviously migrated to the galena surface. Principal component analysis (PCA) of a dataset composed of 206 positive ion peaks of galena and sphalerite indicates that the surface components of galena and sphalerite migrated from either side to different degrees. This study successfully identified an important factor for unintentional activation of lead and zinc minerals during flotation: homogenization of surface components of different minerals during grinding.
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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".