Progress in Sodium Silicates for All‐Solid‐State Sodium Batteries—a Review
Bibliographic record
Abstract
All solid‐state sodium batteries (ASSSBs) are considered a promising alternative to lithium‐ion batteries due to increased safety in employing solid‐state components and the widespread availability and low cost of sodium. As one of the indispensable components in the battery system, organic liquid electrolytes are the currently used electrolytes due to their high‐ionic conductivity (10−2 S cm−1) and good wettability; however, their low‐thermal stability, flammability, and leakage tendency pose safety concerns. The growing sodium‐ion battery technology with solid electrolytes is a viable solution due to their improved safety. However, solid electrolytes suffer from insufficient ionic conductivity at room temperature (10−4–10−3 S cm−1), poor interface stability, high charge‐transfer resistance, and low wettability, yielding inferior battery performance. Sodium rare‐earth silicates are a new class of materials with a 3D structure framework similar to sodium‐superionic conductors (NASICONs). These silicates can be used as a solid electrolyte for solid‐state sodium batteries due to their high‐ionic conduction (10−3 S cm−1) at 25 °C. Herein, the sodium rare‐earth silicate synthesis, crystal structure, ion‐conduction mechanism, doping, and electrochemical properties are discussed. This emerging type of inorganic solid electrolyte can pave the way to building next‐generation ASSSBs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".