Optimisation of fine auriferous pyrite recovery using anionic and non-ionic collectors
Bibliographic record
Abstract
The beneficial effects of the synergy between two or more anionic reagents have long been known. The purpose is to increase both the recovery and selectivity. In this study, the flotation of fine auriferous pyrite particles (<38 μm) was studied using a mixture of anionic and non-ionic collectors. When Z-105 was used for non-ionic collector and mixed with anionic reagents, the flotation results showed a better recovery of auriferous pyrite than that was obtained compared with using collectors alone. The adsorption of anionic/non-ionic collectors mixture and single anionic collectors was investigated. The adsorption density of collectors on auriferous pyrite surface was increased slightly when an anionic/non-ionic collector mixture was used compared with anionic collector alone. IR spectra results showed that no new absorption peaks appeared after the adsorption of Z-105 on auriferous pyrite. Z-105 adsorption occurred in molecular form. When anionic mixture was used for collector together with non-ionic collector (Z-105), a concentrate assaying 12.12 g t−1 Au was produced at Au recovery of 90.8%. Compared with using anionic collector mixture alone, the Au recovery was significantly improved. The use of anionic and non-ionic collector mixtures shows great potential for industrial application.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".