Synergistic, extractive, and selective separation of light, medium, and heavy rare earth elements using Cyanex 572 and Alamine 336 from a chloride medium
Bibliographic record
Abstract
In this study, the solvent extraction of rare earth elements (REEs) of different groups of light (LREE), medium (MREE), and heavy (HREE) from a chloride medium using the mixture of Cyanex 572 (cationic extractant, organophosphorus-based) and Alamine 336 (anionic extractant, amine-based) was studied. The results showed higher extraction efficiency for HREEs when sole Cyanex 572 was used. However, the addition of Alamine 336 to Cyanex 572 increased the extraction efficiency of LREEs and MREEs. The optimal volumetric ratio of Alamine 336 to Cyanex 572 for maximum extraction efficiency, under the studied conditions, was concluded to be 1:1 at a total extractant concentration of 30 % v/v. The synergistic enhancement factor when using this system for the extraction of LREEs and MREEs varied from 2 to 76 at different pH values and Alamine 336 ratios. To analyze the synergistic effect of the addition of Alamine 336 to Cyanex 572 on REE extraction, slope analysis and Fourier transform infrared spectroscopy measurement were performed. It was concluded that the role of Alamine 336 could be that of a proton ion extractor (released from the Cyanex 572 structure during REEs extraction) for REECl 2+ complexes and that of a phase modifier for Cyanex 572 for REECl 2 + complexes.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".