Structural Characterization of AgI–AgPO<sub>3</sub>–Ag<sub>2</sub>WO<sub>4</sub> Superionic Conducting Glasses by Advanced Solid-State NMR Techniques
Bibliographic record
Abstract
Glass samples of composition 40AgI–(60– x )AgPO 3 – x Ag 2 WO 4 (0 ≤ x ≤ 25 mol %) have been prepared by the conventional melt-quenching method. These glasses receive renewed interest due to their ionic conductivity and transparency in the visible range. Because the physical and optical properties of these glasses are highly dependent on composition in this system, a comprehensive structural study has been carried out using Raman spectroscopy and 1D and 2D NMR of the 31 P and 109 Ag nuclei. With increasing Ag 2 WO 4 content, the network is modified from a 1D Q (2) -like chain structure to a topology in which Q (1) and Q (0) species linked to octahedrally coordinated tungsten species dominate. This structural transformation increases the glass rigidity and stability against hydrolysis reactions. The compositional evolution of the phosphate speciation (in terms of Q ( n ) mW units) is consistent with maximum tungstate dispersion in glasses with x ≤ 10, while for glasses with higher tungstate content the data are more consistent with a random distribution of P–O–P, P–O–W, and W–O–W linkages. The 109 Ag NMR chemical shifts are independent of composition and suggest that mobile silver ions are situated within cluster regions, furnishing a constant mixed iodide/oxide local environment.
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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".