MOFs and COFs for Radionuclide and Nuclear‐Waste Treatment
Bibliographic record
Abstract
Abstract The ever‐growing energy demand, driven by rapid industrialization and global urbanization, has escalated the development of sustainable nuclear power generation. Nuclear fission and fuel production generate several radioactive byproducts including 79 Se, 85 Kr, 90 Sr, 99 Tc, 127 Xe, 129/131 I, 137 Cs, 235 U, which pose great threat upon environmental infiltration. The sustainable development of nuclear energy relies on the easy and adequate accessibility of nuclear‐fuels, like uranium, alongside safe and efficient management of the nuclear fuel cycles. To this end, reticular materials such as metal–organic frameworks (MOFs) and covalent–organic frameworks (COFs) have emerged as versatile sorbent platforms for efficient treatment of various radionuclides owing to their structural tunability and target specificity. Given that momentous advances have been made in radionuclide treatment by reticular materials in the past few decades, it is important to systematically review and summarize the recent advancements in this field. In this review, a brief overview of the different classes of radioactive‐wastes, and the principles of radioactive waste treatment is first presented. The prerequisites in materials designing are then discussed, followed by the recent progress in MOFs‐ and COFs‐materials toward radioactive‐waste‐treatment. Finally, future perspectives on the unresolved scientific and technical challenges are proposed, aiming to fast‐track the translation of these materials toward real‐world implementation.
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".