Direct observation of lithium metal dendrites with ceramic solid electrolyte
Bibliographic record
Abstract
Abstract Dendrite formation, which could cause a battery short circuit, occurs in batteries that contain lithium metal anodes. In order to suppress dendrite growth, the use of electrolytes with a high shear modulus is suggested as an ionic conductive separator in batteries. One promising candidate for this application is Li7La3Zr2O12 (LLZO) because it has excellent mechanical properties and chemical stability. In this work, in situ scanning electron microscopy (SEM) technique was employed to monitor the interface behavior between lithium metal and LLZO electrolyte during cycling with pressure. Using the obtained SEM images, videos were created that show the inhomogeneous dissolution and deposition of lithium, which induce dendrite growth. The energy dispersive spectroscopy analyses of dendrites indicate the presence of Li, C, and O elements. Moreover, the cross-section mapping comparison of the LLZO shows the inhomogeneous distribution of La, Zr, and C after cycling that was caused by lithium loss near the Li electrode and possible side reactions. This work demonstrates the morphological and chemical evolution that occurs during cycling in a symmetrical Li–Li cell that contains LLZO. Although the superior mechanical properties of LLZO make it an excellent electrolyte candidate for batteries, the further improvement of the electrochemical stabilization of the garnet–lithium metal interface is suggested.
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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) | 0.002 | 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".