Separation of rare earth elements via pickering emulsion: A sustainable approach to physicochemical beneficiation
Bibliographic record
Abstract
Separating rare earth elements (REE) bearing minerals from their associated gangue minerals, such as dolomite and calcite, is challenging, particularly for fine particle size, due to their similar physicochemical surface properties. To exploit the small differences in surface properties between the gangue and the REE minerals, a solid stabilized emulsification (SSE) process was developed to concentrate the fine REE minerals, bastnaesite, and monazite, from carbonate minerals. Our study examines the minerals’ surface properties, contact angle, and zeta potential, on the resulting mineral-oil–water emulsion systems. The mineral separation occurred naturally, without surface modifiers, using key operating parameters, like moderate agitation (450 rpm), low oil viscosity (< 20 cSt), and a pH ranging from 6 to 10. In the fine ore (< 38 µm), REE-bearing minerals, predominantly liberated or highly exposed, had a strong affinity for the oil phase (contact angle > 59°), compared to the carbonate minerals (contact angle < 48°), which remained in the aqueous phase. As monazite and bastnaesite, particularly monazite, tended to attach to the oil, a 68 % REE recovery and an enrichment ratio of 2.9 occurred in a single-stage emulsification process. This study showcases SSE as a promising sustainable solution for rare earth beneficiation.
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.
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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".