Starch vs. tannin as biodegradable reagents for ultrafine hematite depression
Bibliographic record
Abstract
Enrichment of ultrafine liberated valuable minerals from their associated gangue phases is one of the emerging investigation topics within mineral processing and recycling. Using green flotation reagents and turning processes into eco-friendly systems is also one of the challenges in the green transition of ore beneficiation plants. Starch and Tanin as biodegradable depressants for hematite depression have been commercially used in various iron ore processing plants. However, their depression effects on ultrafine particles were not systemically assessed and compared. To fill this gap, this investigation examined the effects of starch, tannin, their mixtures (different ratios), and their different conditioning sequence on the floatability of ultrafine quartz and hematite (- 15 µm). Since the macromolecular polymer of these biodegradable depressants can bind particles together and flocculate them, turbidity analyses were used to assess their optimum ratio for hematite depression without affecting quartz floatability. Turbidity analyses provided a mixture of tannin and starch might enhance the flotation separation of quartz from hematite. Starch could flocculate ultrafine hematite particles, while tannin could disperse ultrafine quartz particles. Floatability experiments indicated that starch had the highest performance in hematite depression (lowest effect on quartz particles) compared to other conditions. Surface analyses (zeta potential and FTIR) proved floatability outcomes and highlighted starch had stronger adsorption on the hematite surface than tannin.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.004 | 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 teacher head, 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".