Large Pore Mesostructured Organosilica-Phosphonate Hybrids as Highly Efficient and Regenerable Sorbents for Uranium Sequestration
Bibliographic record
Abstract
Potential consequences of radiological/nuclear events on the population and the environment have led the scientific community to rethink its approach toward a monitoring based on radiochemical separation. In this context, there is a great need to design radioanalytical systems for quickly evaluating environmental impacts in case of incidents and nuclear events. Phosphonate-functionalized large pore three-dimensional (3-D) cubic (KIT-6) and two-dimensional (2-D) hexagonal (SBA-15) silicas have been studied as highly efficient uranium extracting adsorbents in acidic media. In both cases, functionalization was performed by grafting (2-diethylphosphatoethyl) triethoxysilane (DPTS) on the mesopore surface of the silica supports. Particular attention was given to comparison of different pore sizes and pore structures and impact on radionuclide extraction, principally through uranium adsorption isotherms and sorption kinetics studies. All hybrid materials demonstrated very fast adsorption kinetics, reaching equilibrium in less than 60 s. Calculated parameters from the Langmuir model revealed a clearly superior performance of the 3-D cubic KIT-6-based sorbents compared to other equivalents, especially for uranium equilibrium concentrations below 50 mg L –1 . Furthermore, a superior maximum adsorption capacity in the range of 54–56 mg of U per gram of sorbent was observed for which it represents almost a 3-fold increase compared to the capacity of commercially available products. High extraction efficiency is demonstrated through dynamic extraction experiments using less than 25 mg of functionalized mesoporous resin analogue. Importantly, the possibility of reusing regenerated mesoporous sorbents is established over several cycles with no loss in uranium extraction capacity suggesting adequate chemical and structural stability of the new sorbent materials.
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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".