Functionalization of Mesoporous Carbon Materials for Selective Separation of Lanthanides under Acidic Conditions
Bibliographic record
Abstract
New functional mesoporous carbon sorbents were successfully synthesized to overcome some issues of solid-liquid extraction (e.g., selectivity, extraction capacity, and reusability under acidic conditions) in production of pure lanthanides (Ln). Wet-oxidation technique was performed to increase the surface reactivity of pristine ordered mesoporous carbon (OMC), and, in a second step, a surface modification using diglycolamide-based (DGA-based) selective ligands toward Ln was performed. Two types of ligands were tested: the first contains a long spacer (e.g., between carbon support and chelating function), and the second has a shorter one. These materials have been characterized by X-ray photoelectron spectroscopy (XPS), low-angle X-ray diffraction (XRD), thermogravimetric analysis, nitrogen sorption, transmission electron microscopy (TEM), and scanning electron microscopy (SEM). These analyses confirmed that the carbon mesostructure was maintained after organo-functionalization of the surface and showed the covalent attachment of selective ligands. These new materials, and especially the system with a short spacer between the ligand and the surface, reveal unique Ln selectivity profiles with improved extraction performances for the recovery of lanthanides, in terms of both selectivity and adsorption capacity, and unprecedented stability under acidic conditions.
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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) | 0.001 | 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".