High Performance Lithium Metal Anode with a Nanolayer of LiZn Alloy for All‐Solid‐State Batteries
Bibliographic record
Abstract
Abstract All‐solid‐state batteries (ASSB) require stable and safe lithium (Li) metal anode, which needs surface preparation to increase lithium diffusion and impede the formation of dendrites. In this work, the formation of a thin LiZn layer on lithium metal using sputter deposition is reported. This method was selected due to the absence of solvents and by‐products generated during the modification, for its rapidity and because the formation of the alloy is performed in a clean and controlled atmosphere. Zinc has been chosen for its low cost and high Li+ ion diffusion coefficient of the corresponding LiZn alloy that is 1000 times higher than Li. Different parameters for the Zn deposition were investigated such as the distance between the Zn target and Li foil, the effect of substrate tilt and the direct current applied to the target. Electrochemical performance of LiFePO4/solid polymer electrolyte/Li ASSB demonstrated the superiority of the LiZn anodes and the clear influence of deposition parameters on the durability and performance at high C‐rates. Scanning electron microscopy images of the cross‐sectional view of LFP/SPE/Li stackings extracted from pouch cells after cycling showed an evident migration of Zn into the bulk Li metal anode as well as the formation of AlZn nanoparticles.
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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".