Drop That Activation Energy: Tetragonal to Cubic Transformations in Na<sub>3</sub>PS<sub>4−</sub><i><sub>x</sub></i>Se<i><sub>x</sub></i> for Solid State Sodium Ion Battery Materials
Bibliographic record
Abstract
Abstract Sodium‐containing chalcogenide materials are emerging as a class of solid electrolytes for application in inexpensive all‐solid‐state sodium‐ion batteries due to their high ionic conductivity, abundance, and degree of synthetic and structural variability. Members of the solid solution Na 3 PS 4− x Se x , which are promising solid electrolytes for sodium‐ion batteries, are prepared by reaction at high temperature. With increasing substitution of S by Se, the structure transforms from tetragonal (space group P 2 1 c for x = 0, 1) to cubic (space group I 3 m for x = 2, 3, 4). Within the solid solution, the S and Se atoms are completely disordered in the environments around the P atoms, in accordance with a binomial distribution, as inferred by the 31 P nuclear magnetic resonance (NMR) spectra. In 23 Na NMR experiments conducted at different magnetic fields and temperatures, quadrupolar lineshapes are observed that are influenced by sodium ion dynamics; the activation energies decrease from 0.21 to 0.15 eV on progressing from the S‐ to the Se‐rich members. A dynamic model is proposed to account for the changes in the 23 Na quadrupolar lineshapes by switching the orientations of the electric field gradient and chemical shift anisotropy tensors when Na + ions hop to the four nearest Na sites.
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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) | 0.011 | 0.001 |
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".