Selective Separation and Preconcentration of Scandium with Mesoporous Silica
Bibliographic record
Abstract
Separation and preconcentration of scandium (Sc) were successfully achieved using a mesoporous silica support that showed good selectivity for this element. Unmodified mesoporous silica materials were used as an extracting medium in a solid-liquid extraction (SLE) process. Selectivity, extraction capacity, kinetics of extraction, and reusability under acidic conditions were investigated. The results demonstrate the potential of unmodified mesoporous silica materials for the selective separation and preconcentration of Sc. As no chelating ligand was grafted on the silica surface, which is often the case for most solid-phase extraction media for metal-ion separation, the experimental data allow us to hypothesize that the accessible silanols on the material surface are responsible for the selective Sc extraction. This interesting feature would drastically decrease the cost of solid-liquid extraction systems by using unmodified mesoporous silica materials. Moreover, a leachate solution obtained from a real rare-earth element ore was used to determine the performances of the proposed materials in a packed column configuration. The maximum Sc adsorption on the silica material surfaces is moderate (1 mg/g), but it is balanced by a great concentration factor (more than 100 times). The extraction performances are potentially promising, both in terms of selectivity and preconcentration, under the acidic conditions tested.
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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".