Unraveling Sodium-Ion Dynamics in Honeycomb-Layered Na<sub>2</sub>Mg<sub><i>x</i></sub>Zn<sub>2–<i>x</i></sub>TeO<sub>6</sub> Solid Electrolytes with Solid-State NMR
Bibliographic record
Abstract
All-solid-state sodium-ion batteries (SIBs) have the potential to offer large-scale, safe, cost-effective, and sustainable energy storage solutions by supplementing the industry-leading lithium-ion batteries. However, for the enhanced bulk properties of SIB components (e.g., solid electrolytes), a comprehensive understanding of their atomic-scale structure and the dynamic behavior of sodium (Na) ions is essential. Here, we utilize a robust multinuclear ( 23 Na, 125 Te, 25 Mg, and 67 Zn) magnetic resonance approach to explore a novel Mg/Zn homogeneously mixed-cation honeycomb-layered oxide Na 2 Mg x Zn 2– x TeO 6 solid solution series. These new intermediate compounds exhibit tailorable bulk Na-ion conductivity (σ) with the highest σ = 0.14 × 10 –4 S cm –1 for Na 2 MgZnTeO 6 at room temperature suitable for SIB solid electrolyte applications as observed by powder electrochemical impedance spectroscopy (EIS). A combination of powder X-ray diffraction (XRD), energy-dispersive X-ray (EDX) spectroscopy, and field emission scanning electron microscopy (FESEM) reveals highly crystalline phase-pure compounds in the P 6 3 22 space group. We show that the Mg/Zn disorder is random within the honeycomb layers using 125 Te nuclear magnetic resonance (NMR) and resolve multiple Na sites using two-dimensional (triple-quantum magic-angle spinning (3QMAS)) 23 Na NMR. The medium-range disorder in the honeycomb layer is revealed through the combination of 25 Mg and 67 Zn NMR, complemented by electronic structure calculations using density functional theory (DFT). Furthermore, we expose very fast local Na-ion hopping processes (hopping rate, 1/τ NMR = 0.83 × 10 9 Hz) by using a laser to achieve variable high-temperature (∼860 K) 23 Na NMR, which are sensitive to different Mg/Zn ratios. The Na 2 MgZnTeO 6 with maximum Mg/Zn disorder displays the highest short-range Na-ion dynamics among all of the solid solution members.
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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) | 0.000 | 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".