Tuning the Anode–Electrolyte Interface Chemistry for Garnet‐Based Solid‐State Li Metal Batteries
Bibliographic record
Abstract
Abstract Lithium (Li) metal is a promising candidate as the anode for high‐energy‐density solid‐state batteries. However, interface issues, including large interfacial resistance and the generation of Li dendrites, have always frustrated the attempt to commercialize solid‐state Li metal batteries (SSLBs). Here, it is reported that infusing garnet‐type solid electrolytes (GSEs) with the air‐stable electrolyte Li3PO4 (LPO) dramatically reduces the interfacial resistance to ≈1 Ω cm2 and achieves a high critical current density of 2.2 mA cm−2 under ambient conditions due to the enhanced interfacial stability to the Li metal anode. The coated and infused LPO electrolytes not only improve the mechanical strength and Li‐ion conductivity of the grain boundaries, but also form a stable Li‐ion conductive but electron‐insulating LPO‐derived solid‐electrolyte interphase between the Li metal and the GSE. Consequently, the growth of Li dendrites is eliminated and the direct reduction of the GSE by Li metal over a long cycle life is prevented. This interface engineering approach together with grain‐boundary modification on GSEs represents a promising strategy to revolutionize the anode–electrolyte interface chemistry for SSLBs and provides a new design strategy for other types of 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".