A critical review on solvent extraction of rare earths from aqueous solutions
Bibliographic record
Abstract
Rare earth elements have unique physicochemical properties that make them essential elements in many high-tech components. Bastnesite (La, Ce)FCO 3 , monazite, (Ce, La, Y, Th)PO 4 , and xenotime, YPO 4 , are the main commercial sources of rare earths. Rare earth minerals are usually beneficiated by flotation or gravity or magnetic processes to produce concentrates that are subsequently leached with aqueous inorganic acids, such as HCl, H 2 SO 4 , or HNO 3 . After filtration or counter current decantation (CCD), solvent extraction is usually used to separate individual rare earths or produce mixed rare earth solutions or compounds. Rare earth producers follow similar principles and schemes when selecting specific solvent extraction routes. The use of cation exchangers, solvation extractants, and anion exchangers, for separating rare earths has been extensively studied. The choice of extractants and aqueous solutions is influenced by both cost considerations and requirements of technical performance. Commercially, D2EHPA, HEHEHP, Versatic 10, TBP, and Aliquat 336 have been widely used in rare earth solvent extraction processes. Up to hundreds of stages of mixers and settlers may be assembled together to achieve the necessary separations. This paper reviews the chemistry of different solvent extractants and typical configurations for rare earth separations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".