Oxychloride Polyanion Clustered Solid‐State Electrolytes via Hydrate‐Assisted Synthesis for All‐Solid‐State Batteries
Bibliographic record
Abstract
Abstract Solid‐state electrolytes (SSEs) play a vital role in the development of high‐energy all‐solid‐state batteries. However, most adopted mechanical ball milling and/or high‐temperature annealing are ineffective approaches for large‐scale synthesis. Herein, a universal and scalable hydrate‐assisted strategy for the synthesis of oxychloride SSEs is developed based on the chemical reaction among alkali chlorides, AlCl3, and AlCl3·6H2O. The synthesized aluminum‐based oxychloride SSEs possess a high Li+ conductivity over 1 mS cm−1 at 30 °C. The final aluminum‐based oxychloride SSEs are structurally heterogeneous with nm‐sized LiCl‐like and LiAlCl4 crystallites and large amounts of amorphous [AlaObClc](2b+c−3a)− components. Faster local mobility of Li+ ions in amorphous structures is verified and is attributable to weakened Li+‐X− interactions ensured by the [AlaObClc](2b+c−3a)− polyanions. The potential applications for this synthesis technique are further demonstrated by kilogram‐scale reactions and synthesis of other oxychloride SSEs including zirconium‐based and tantalum‐based analogs. These findings not only provide a new simple, scalable, and energy‐efficient synthesis route for oxychloride SSEs but also further promote their application in all‐solid‐state batteries.
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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 | 0.001 |
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".