Rare Earth Elements Recovery Using Selective Membranes via Extraction and Rejection
Bibliographic record
Abstract
Recently, demands for raw materials like rare earth elements (REEs) have increased considerably due to their high potential applications in modern industry. Additionally, REEs' similar chemical and physical properties caused their separation to be difficult. Numerous strategies for REEs separation such as precipitation, adsorption and solvent extraction have been applied. However, these strategies have various disadvantages such as low selectivity and purity of desired elements, high cost, vast consumption of chemicals and creation of many pollutions due to remaining large amounts of acidic and alkaline wastes. Membrane separation technology (MST), as an environmentally friendly approach, has recently attracted much attention for the extraction of REEs. The separation of REEs by membranes usually occurs through three mechanisms: (1) complexation of REE ions with extractant that is embedded in the membrane matrix, (2) adsorption of REE ions on the surface created-active sites on the membrane and (3) the rejection of REE ions or REEs complex with organic materials from the membrane. In this review, we investigated the effect of these mechanisms on the selectivity and efficiency of the membrane separation process. Finally, potential directions for future studies were recommended at the end of the review.
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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) | 0.001 | 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".