Correlated Migration Invokes Higher Na<sup>+</sup>‐Ion Conductivity in NaSICON‐Type Solid Electrolytes
Bibliographic record
Abstract
Abstract Na super ion conductor (NaSICON), Na 1+ n Zr 2 Si n P 3– n O 12 is considered one of the most promising solid electrolytes; however, the underlying mechanism governing ion transport is still not fully understood. Here, the existence of a previously unreported Na5 site in monoclinic Na 3 Zr 2 Si 2 PO 12 is unveiled. It is revealed that Na + ‐ions tend to migrate in a correlated mechanism, as suggested by a much lower energy barrier compared to the single‐ion migration barrier. Furthermore, computational work uncovers the origin of the improved conductivity in the NaSICON structure, that is, the enhanced correlated migration induced by increasing the Na + ‐ion concentration. Systematic impedance studies on doped NaSICON materials bolster this finding. Significant improvements in both the bulk and total ion conductivity (e.g., σ bulk = 4.0 mS cm −1 , σ total = 2.4 mS cm −1 at 25 °C) are achieved by increasing the Na content from 3.0 to 3.30–3.55 mol formula unit −1 . These improvements stem from the enhanced correlated migration invoked by the increased Coulombic repulsions when more Na + ‐ions populate the structure rather than solely from the increased mobile ion carrier concentration. The studies also verify a strategy to enhance ion conductivity, namely, pushing the cations into high energy sites to therefore lower the energy barrier for cation migration.
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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".