Biodegradable hematite depressants for green flotation separation – An overview
Bibliographic record
Abstract
Due to environmental issues and the restrictions imposed on mineral flotation separation, the use of biodegradable and environmentally friendly reagents has gained widespread international attention. So far, several investigations have been conducted regarding the eco-friendly flotation separation of iron oxide ores for moving toward sustainable development and cleaner production. Yet, no critical review is specified on the green and eco-friendly depression reagents through their reverse flotation beneficiation. Therefore, this study will comprehensively discuss the previously conducted works in this area and provides suggestions for future assessments and developments. This robust study explored various adsorption aspects of natural-based depressants (polysaccharide-, polyphenolic-, and lignosulfonate-based) on iron oxide minerals (mainly hematite) to create a possible universal trend for each biodegradable depressant derivative. The laboratory and industrial experiments indicated that these depressants (except lignosulfonate-based) could selectively depress hematite at alkaline pHs and enhance its reverse flotation separation from their gangue phases (especially silicates as the main gangue phases). Although these eco-friendly depressants showed promising metallurgical results, several gaps still need to be addressed, notably in surface analyses and their adsorption mechanisms.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| 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".