Localized Electrolyte Grain Engineering to Suppress Li Intrusion in All‐Solid‐State Batteries
Bibliographic record
Abstract
Abstract Li intrusion is the primary factor contributing to the undesirable cycling durability and rate capability of all‐solid‐state lithium metal batteries. However, conventional engineering methodologies for solid electrolytes (SEs) that focus on crystalline scales, such as doping, have limited efficacy in addressing this issue, as they not only involve cumbersome trial‐and‐error processes but also struggle to simultaneously optimize the multiple macroscopic properties necessary for effectively suppressing Li intrusion. Herein, rather than following the conventional practice of SE engineering, it is concentrated on optimizing SEs at the grain‐aggregate level. A highly scalable chemical approach based on a thermodynamic‐favored anion exchange reaction is first developed to engineer an amorphous metal compound layer on the surface of argyrodite‐type electrolyte grains. Further, a novel localized grain engineering concept is introduced, which combines engineered and pure electrolyte grains to enable aggregates with favorable macroscopic properties for suppressing Li intrusion. The localized grain‐engineered electrolyte aggregates greatly enhance Li reversibility and are able to suppress Li intrusion under practical working conditions. Notably, the 20 µm‐Li||LiNi 0.83 Co 0.12 Mn 0.05 O 2 cell using localized grain‐engineered electrolyte aggregates can stably cycle for over 2000 cycles at a high current density of 1.6 mA cm −2 .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".