Exploring Dextrin as an Eco-Friendly Depressant for Selective Flotation Separation of Scheelite and Calcite Minerals
Bibliographic record
Abstract
Depressants are essential additives in the flotation separation of scheelite-calcite minerals. However, traditional inorganic depressants such as sodium silicate have the disadvantages of high dosage, environmental pollution, and being non-efficient, which leads to a growing interest in eco-friendly and effective organic alternatives. In this study, a polysaccharide, dextrin, was used as a green depressant for the flotation separation of scheelite from calcite. Micro-flotation experiments indicated that dextrin selectively depressed calcite at natural pH yet scheelite remained floatable using sodium oleate (NaOL) as a collector. Adsorption density and zeta potential results indicated that dextrin was preferentially adsorbed on the calcite surface and prevented the subsequent NaOL from adsorption. By contrast, dextrin had a weak interaction with scheelite, allowing NaOL to be adsorbed on the scheelite surface. Fourier transform infrared spectroscopy (FTIR) analysis and density functional theory (DFT) calculations suggested that the Ca2+ active sites on the calcite surface interacted with -OH groups on the carbon ring of dextrin. X-ray photoelectron spectroscopy (XPS) tests confirmed the chemical interaction between -OH groups in dextrin and Ca2+ active sites on the mineral surfaces, and importantly, the interaction was much stronger for calcite than for scheelite.
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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) | 0.000 | 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 | 0.001 | 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".