Utilization and Mechanisms of Tannic Acid as a Depressant for Chalcopyrite and Pyrite Separation
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Current flotation practices using lime or cyanide as depressants in chalcopyrite and pyrite separation have significant disadvantages, such as substantial reagent consumption, high slurry pH, and environmental hazards. This work aimed to explore the utilization and mechanisms of tannic acid (TA) as an eco-friendly alternative to lime or cyanide in chalcopyrite–pyrite separation. Flotation results showed that TA selectively depressed pyrite yet allowed chalcopyrite to float at neutral or alkaline pH. Adsorption density and zeta potential results indicated that TA adsorbed intensely on pyrite but minorly on chalcopyrite. Besides, potassium ethyl xanthate was still largely adsorbed on chalcopyrite but not on pyrite after TA adsorption. Surface analysis by Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy further showed that the oxidation species of FeOOH and Fe 2 (SO 4 ) 3, particularly FeOOH were the main active sites for TA chemical adsorption. Owing to the greater and faster oxidation of pyrite, more FeOOH and Fe 2 (SO 4 ) 3 were generated on the pyrite surface, and the chemical adsorption of TA was more pronounced on the pyrite surface than on the chalcopyrite surface.
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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) | 0.000 | 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".