Lithium and Chlorine-Rich Preparation of Mechanochemically Activated Antiperovskite Composites for Solid-State Batteries
Bibliographic record
Abstract
Assembling all-solid-state batteries presents a unique challenge due to chemical and electrochemical complexities of interfaces between a solid electrolyte and electrodes. While the interface stability is dictated by thermodynamics, making use of passivation materials often delays interfacial degradation and extends the cycle life of all-solid cells. In this work, we investigated antiperovskite lithium oxychloride, Li3OCl, as a promising passivation material that can engineer the properties of solid electrolyte-Li metal interfaces. Our experiment to obtain stoichiometric Li3OCl focuses on how the starting ratios of lithium and chlorine and mechanochemical activation affect the phase stability. For substantial LiCl excess conditions, the antiperovskite phase was found to form by simple melt-quenching and subsequent high-energy ball-milling. Li3OCl prepared with 100% excess LiCl exhibits ionic conductivity of 3.2×10-5 S cm-1 at room temperature, as well as cathodic stability against Li metal upon the extended number of cycling. With a conductivity comparable to other passivation layers, and stable interface properties, our Li3OCl/LiCl composite has the potential to stably passivate the solid-solid interfaces 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 | 0.000 |
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".