A Surface Chemistry Investigation into Depressants for Minerals Associated with Pyrochlore
Bibliographic record
Abstract
Niobium (Nb), a transition element, has been applied mainly as steel additive, among other cutting-edge applications. Nb is mainly produced from pyrochlore-containing ores, dominated by mines at Araxá, Catalão (both from Brazil), and Niobec (Saguenay Region, QC, Canada). At these plants, flotation is employed as the main beneficiation method that all plants apply direct pyrochlore flotation; Catalão and Niobec apply additional reverse flotation prior to pyrochlore flotation. During flotation, depressants are added to improve selectivity, which highlights their importance to Nb mineral flotation. However, most of the available studies related to Nb mineral flotation focus on collectors; the knowledge on depressants is limited. In the present work, various depressants, including sodium silicate, oxalic acid, F100, starch, carboxymethyl cellulose (CMC), and chitosan, are compared for pyrochlore flotation at pH 7 in the presence of sodium oleate and dodecylamine (DDA) collectors. The results are compared with common gangue minerals, including dolomite, calcite, and hematite. It was observed that the performance of depressants is related to the collector applied, which was justified by the mineral surface charge after depressant adsorption and the charge of the collector. Among the tested combinations, 5 kg/t F100 + 2 kg/t DDA and 5 kg/t CMC + 2 kg/t DDA showed potential selectivity toward pyrochlore and hematite, whereas both carbonate minerals could be successfully depressed. Zeta potential measurement and X-ray photoelectron spectroscopy were applied to understand the interaction between depressants and the model minerals.
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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".