Highly Efficient and Selective Recovery of Rare Earth Elements Using Mesoporous Silica Functionalized by Preorganized Chelating Ligands
Bibliographic record
Abstract
Separating the rare earth elements (REEs) in an economically and environmentally sustainable manner is one of the most pressing technological issues of our time. Herein, a series of preorganized bidentate phthaloyl diamide (PA) ligands was synthesized and grafted on large-pore 3-dimensional (3-D) KIT-6 mesoporous silica. The synthesized sorbents were fully characterized by N 2 physisorption, FT-IR, 13 C cross-polarization (CP) and 29 Si magic-angle spinning (MAS) NMR, thermogravimetric analysis-differential thermal analysis (TGA-DTA), and elemental analysis. Overall, the grafting of PA-type ligands was found to have significantly improved the extraction performance of the sorbents toward REEs compared to the homogeneous analogues. Specifically, the sorbent modified with the 1,2-phtaloyl ligand shows high preference over lanthanides with smaller size, whereas the 1,3-phtaloyl ligand exhibits selectivity toward elements with larger ion radius. This selectivity drastically changes from the homogeneous models that do not exhibit any selectivity. The possibility of regenerating the mesoporous sorbents through simple stripping using oxalate salt is demonstrated over up to 10 cycles with no significant loss in REEs extraction capacity, suggesting adequate chemical and structural stability of the new sorbent materials. Despite the complex ion matrix and high ionic composition, the exposure of industrial mining deposits containing REEs to the sorbents results in selective recovery of target REEs.
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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) | 0.000 | 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 | 0.000 |
| 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".