Antiperovskite Electrolytes for Solid-State Batteries
Bibliographic record
Abstract
Solid-state batteries have fascinated the research community over the past decade, largely due to their improved safety properties and potential for high-energy density. Searching for fast ion conductors with sufficient electrochemical and chemical stabilities is at the heart of solid-state battery research and applications. Recently, significant progress has been made in solid-state electrolyte development. Sulfide-, oxide-, and halide-based electrolytes have been able to achieve high ionic conductivities of more than 10 –3 S/cm at room temperature, which are comparable to liquid-based electrolytes. However, their stability toward Li metal anodes poses significant challenges for these electrolytes. The existence of non-Li cations that can be reduced by Li metal in these electrolytes hinders the application of Li anode and therefore poses an obstacle toward achieving high-energy density. The finding of antiperovskites as ionic conductors in recent years has demonstrated a new and exciting solution. These materials, mainly constructed from Li (or Na), O, and Cl (or Br), are lightweight and electrochemically stable toward metallic Li and possess promising ionic conductivity. Because of the structural flexibility and tunability, antiperovskite electrolytes are excellent candidates for solid-state battery applications, and researchers are still exploring the relationship between their structure and ion diffusion behavior. Herein, the recent progress of antiperovskites for solid-state batteries is reviewed, and the strategies to tune the ionic conductivity by structural manipulation are summarized. Major challenges and future directions are discussed to facilitate the development of antiperovskite-based 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".