Functionalization of mesoporous materials for lanthanide and actinide extraction
Bibliographic record
Abstract
Among the energy sources currently available that could address our insatiable appetite for energy and minimize our CO2 emission, solar, wind, and nuclear energy currently occupy an increasing portion of our energy portfolio. The energy associated with these sources can however only be harnessed after mineral resources containing valuable constituents such as actinides (Ac) and rare earth elements (REEs) are extracted, purified and transformed into components necessary for the conversion of energy into electricity. Unfortunately, the environmental impacts resulting from their manufacture including the generation of undesirable and, sometimes, radioactive wastes and the non-renewable nature of the mineral resources, to name a few, have emerged as challenges that should be addressed by the scientific community. In this perspective, the recent development of functionalized solid materials dedicated to selective elemental separation/pre-concentration could provide answers to several of the above-mentioned challenges. This review focuses on recent advances in the field of mesoporous solid-phase (SP) sorbents designed for REEs and Ac liquid-solid extraction. Particular attention will be devoted to silica and carbon sorbents functionalized with commonly known ligands, such as phosphorus or amide-containing functionalities. The extraction performances of these new systems are discussed in terms of sorption capacity and selectivity. In order to support potential industrial applications of the silica and carbon-based sorbents, their main drawbacks and advantages are highlighted and discussed.
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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".