Polysaccharides-based pyrite depressants for green flotation separation: An overview
Bibliographic record
Abstract
Froth flotation is an essential processing technique for upgrading low-grade ores. Flotation separation would not be efficient without chemical surfactants (collectors, depressants, frothers, etc.). Depressants play a critical role in the selective separation of minerals in that they deactivate unfavorable mineral surfaces and hinder them from floating into the flotation concentration zone. Pyrite is the most common and challenging sulfide gangue, and its conventional depressants could be highly harmful to nature and humans. Therefore, using available, affordable, eco-friendly polymers to assist or replace hazardous reagents is mandatory for a green transition. Polysaccharide-based (starch, dextrin, carboxymethyl cellulose, guar gum, etc.) polymers are one of the most used biodegradable depressant groups for pyrite depression. Despite the satisfactory flotation results obtained using these eco-friendly depressants, several gaps still need to be addressed, specifically in investigating surface interactions, adsorption mechanisms, and parameters affecting their depression performance. As a unique approach, this review comprehensively discussed previously conducted studies on pyrite depression with polysaccharide-based reagents. Additionally, practical suggestions have been provided for future assessments and developments of polysaccharide-based depressants, which pave the way to green flotation. This robust review also explored the depression efficiency and various adsorption aspects of naturally derived depressants on the pyrite surface to create a possible universal trend for each biodegradable depressant derivative.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".