Interplay between noble gases and MOFs: Insights from <sup>129</sup> Xe and <sup>83</sup> Kr NMR spectroscopy
Bibliographic record
Abstract
Efficient separation and storage of radioactive noble gas isotopes [krypton-85 ( 85 Kr) and xenon-131m/133/135 ( 131m/133/135 Xe)] from off-gas during spent nuclear fuel reprocessing are vital for environmental protection and noble gas recovery. Metal-organic frameworks (MOFs) are promising adsorbents, yet atomic-level insights into Xe and Kr adsorption remain limited. Here, we report the first application of high-field solid-state 83 Kr nuclear magnetic resonance (NMR), alongside extensive 129 Xe NMR, to investigate gas adsorption in eight MOFs representing three design strategies: ultramicroporous one-dimensional–channel MOFs with pore sizes matching noble gas diameters, functionalized MOFs, and MOFs with open metal sites. Single-gas and coadsorption studies reveal distinct adsorption sites, guest-host interactions, and gas dynamics in each MOF. Most MOFs retain crystallinity after exposure to 60-kilogray γ radiation. Several radiation-sensitive MOFs exhibit enhanced stability in the presence of Xe, suggesting that Xe adsorption enhances framework stability and may broaden the range of MOFs usable under γ radiation in nuclear off-gas separation. These findings offer valuable molecular-level design insights.
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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 | 0.001 |
| 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".