Termination-dependent surface chemistry of pyrochlore flotation: stability, hydration, and collector adsorption
Bibliographic record
Abstract
Pyrochlore, the primary mineral in niobium-bearing ores, generates diverse surface terminations during comminution, which can influence subsequent froth flotation. Using density functional theory (DFT) simulations, we evaluated the stability of all possible low-index pyrochlore surface terminations and analyzed their interactions with water and amine collectors. The simulations revealed that oxygen-terminated surfaces are more stable than metal-exposed ones. We also examined hydration behavior, which precedes collector interaction during flotation. Oxygen-terminated surfaces readily undergo hydroxylation, forming strong covalent bonds and dense hydration layers, while metal-terminated surfaces exhibit weaker hydrogen bonding and greater hydrophobicity. To simulate pH-dependent flotation conditions, we investigated the adsorption of neutral and protonated amines on hydroxylated (hydrophilic) and bare (hydrophobic) surfaces. Importantly, this study is the first to explicitly consider twin surfaces—the metastable counterparts of thermodynamically favored planes that are generated by mechanical breakage—and to reveal their distinct surface terminations and reactivity. Contrary to the prevailing assumption that Nb–N bonding dominates, our results revealed multiple adsorption mechanisms. Notably, hydrophilic surfaces exhibited enhanced adsorption, especially in the presence of protonated collectors. These results emphasize the importance of considering termination-specific adsorption pathways and collector speciation when designing effective pyrochlore reagents. Elucidating these surface-dependent interactions provides new insights for the development of selective reagents.
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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 | 0.001 |
| 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".