Liquid−Liquid Phase Separation in Model Nuclear Waste Glasses: A Solid-State Double-Resonance NMR Study
Bibliographic record
Abstract
Double-resonance nuclear magnetic resonance (NMR) techniques are used in addition to single-resonance NMR experiments to probe the degree of mixing between network-forming cations Si and B, along with the modifier cations Cs + and Na + in two molybdenum-bearing model nuclear waste glasses. The double-resonance experiments involving 29 Si in natural abundance are made possible by the implementation of a CPMG pulse-train during the acquisition period of the usual REDOR experiments. For the glass with lower Mo content, the NMR results show a high degree of Si−B mixing, as well as an homogeneous distribution of the cations within the borosilicate network, characteristic of a non-phase-separated glass. For the higher-Mo glass, a decrease of B−Si(Q 4 ) mixing is observed, indicating phase separation. 23 Na and 133 Cs NMR results show that although the Cs + cations, which do not seem to be influenced by the molybdenum content, are spread within the borate network, there is a clustering of the Na + cations, very likely around the molybdate units. The segregation of a Mo-rich region with Na + cations appears to shift the bulk borosilicate glass composition toward the metastable liquid−liquid immiscibility region and induce additional phase separation. Although no crystallization is observed in the present case, this liquid−liquid phase separation is likely to be the first stage of crystallization that can occur at higher Mo loadings or be driven by heat−treatment. From this study emerges a consistent picture of the nature and extent of such phase separation phenomena in Mo-bearing glasses, and demonstrates the potential of double−resonance NMR methods for the investigation of phase separation in amorphous 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".