Lithium-compatible and air-stable vacancy-rich Li <sub>9</sub> N <sub>2</sub> Cl <sub>3</sub> for high–areal capacity, long-cycling all–solid-state lithium metal batteries
Bibliographic record
Abstract
Attaining substantial areal capacity (>3 mAh/cm 2 ) and extended cycle longevity in all–solid-state lithium metal batteries necessitates the implementation of solid-state electrolytes (SSEs) capable of withstanding elevated critical current densities and capacities. In this study, we report a high-performing vacancy-rich Li 9 N 2 Cl 3 SSE demonstrating excellent lithium compatibility and atmospheric stability and enabling high–areal capacity, long-lasting all–solid-state lithium metal batteries. The Li 9 N 2 Cl 3 facilitates efficient lithium-ion transport due to its disordered lattice structure and presence of vacancies. Notably, it resists dendrite formation at 10 mA/cm 2 and 10 mAh/cm 2 due to its intrinsic lithium metal stability. Furthermore, it exhibits robust dry-air stability. Incorporating this SSE in Ni-rich LiNi 0.83 Co 0.11 Mn 0.06 O 2 cathode-based all–solid-state batteries, we achieve substantial cycling stability (90.35% capacity retention over 1500 cycles at 0.5 C) and high areal capacity (4.8 mAh/cm 2 in pouch cells). These findings pave the way for lithium metal batteries to meet electric vehicle performance demands.
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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".