Polysaccharides-based pyrite depressants for green flotation separation: An overview
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it