Large Pore Mesostructured Organosilica-Phosphonate Hybrids as Highly Efficient and Regenerable Sorbents for Uranium Sequestration
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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 it