Insight into Ion Diffusion Dynamics/Mechanisms and Electronic Structure of Highly Conductive Sodium-Rich Na<sub>3+<i>x</i></sub>La<sub><i>x</i></sub>Zr<sub>2–<i>x</i></sub>Si<sub>2</sub>PO<sub>12</sub> (0 ≤ <i>x</i> ≤ 0.5) Solid-State Electrolytes
Bibliographic record
Abstract
Solid-state electrolytes (SSEs) have attracted considerable attention as an alternative for liquid electrolytes to improve safety and durability. Sodium Super Ionic CONductor (NASICON)-type SSEs, typically Na 3 Zr 2 Si 2 PO 12, have shown great promise because of their high ionic conductivity and low thermal expansivity. Doping La into the NASICON structure can further elevate the ionic conductivity by an order of magnitude to several mS/cm. However, the underlying mechanism of ionic transportation enhancement has not yet been fully disclosed. Herein, we fabricate a series of Na 3+ x La x Zr 2– x Si 2 PO 12 (0 ≤ x ≤ 0.5) SSEs. The electronic and local structures of constituent elements are studied via synchrotron-based X-ray absorption spectroscopy, and the ionic dynamics and Na-ion conduction mechanism are investigated by solid-state nuclear magnetic resonance spectroscopy. The results prove that La 3+ ions exist in the form of phosphate impurities such as Na 3 La(PO 4 ) 2 instead of occupying the Zr 4+ site. As a result, the increased Si/P ratio in the NASICON phase, accompanied by an increase in the sodium ion occupancy, makes a major contribution to the enhancement of ionic conductivity. The spin–lattice relaxation time study confirms the accelerated Na + motions in the altered NASICON phase. Modifications on the Si/P composition can be a promising strategy to enhance the ionic conductivity of NASICON.
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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".