Origin of High Ionic Conductivity of Sc‐Doped Sodium‐Rich NASICON Solid‐State Electrolytes
Bibliographic record
Abstract
Abstract Substitution of liquid electrolyte with solid‐state electrolytes (SSEs) has emerged as a very urgent and challenging research area of rechargeable batteries. NASICON (Na3Zr2Si2PO12) is one of the most potential SSEs for Na‐ion batteries due to its high ionic conductivity and low thermal expansion. It is proven that the ionic conductivity of NASICON can be improved to 10−3 S cm−1 by Sc‐doping, of which the mechanism, however, has not been fully understood. Herein, a series of Na3+xScxZr2−xSi2PO12 (0 ≤ x ≤ 0.5) SSEs are prepared. To gain a deep insight into the ion transportation mechanism, synchrotron‐based X‐ray absorption spectroscopy (XAS) is employed to characterize the electronic structure, and solid‐state nuclear magnetic resonance (SS‐NMR) is used to analyze the dynamics. In this study, Sc is successfully doped into Na3Zr2Si2PO12 to substitute Zr atoms. The redistribution of sodium ions at certain specific sites is proven to be critical for sodium ion movement. For x ≤ 0.3, the promotion of sodium ion movement is attributed to sodium ion concentration increase at the Na2 sites and decrease at the Na1 and Na3 sites. For x > 0.3, the inhibition of sodium ion movement is due to the phase change from monoclinic to rhombohedral and an increasing impurity content.
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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".