Recent Advances in the Separation of Rare Earth Elements Using Mesoporous Hybrid Materials
Bibliographic record
Abstract
Over the past decades, the need for rare earth elements (REEs) has increased substantially, mostly because these elements are used as valuable additives in advanced technologies. However, the difference in ionic radius between neighboring REEs is small, which renders an efficient sized-based separation extremely challenging. Among different types of extraction methods, solid-phase extraction (SPE) is a promising candidate, featuring high enrichment factor, rapid adsorption kinetics, reduced solvent consumption and minimized waste generation. The great challenge remains yet to develop highly efficient and selective adsorbents for this process. In this regard, ordered mesoporous materials (OMMs) possess high specific surface area, tunable pore size, large pore volume, as well as stable and interconnected frameworks with active pore surfaces for functionalization. Such features meet the requirements for enhanced adsorbents, not only providing huge reactional interface and large surface capable of accommodating guest species, but also enabling the possibility of ion-specific binding for enrichment and separation purposes. This short personal account summarizes some of the recent advances in the use of porous hybrid materials as selective sorbents for REE separation and purification, with particular attention devoted to ordered mesoporous silica and carbon-based sorbents.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 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".