To Be or Not to Be: Dendrite Growth Mechanism Adjacent to Pits for High Stability of Lithium Metal Anode
Bibliographic record
Abstract
The challenge of dendrite growth is a formidable obstacle that leads to short circuits when charging lithium (Li)-metal batteries. Implementing patterns with various pit structures on Li metal proves to be an effective strategy to alleviate dendrite growth. Nonetheless, the growth mechanism of Li dendrites near pits is still under debate, making it difficult to accurately propose patterns to enhance the stability of the Li metal. In this study, the growth mechanism of dendrites near the pits is successfully clarified for the first time using the in situ optical microscopy method and electrochemical modeling. A direct correlation is established between the formation of Li dendrites and factors. Areas with heightened electric field strength and localized current density are more susceptible to dendrite formation. Increased local surface roughness promotes dendrite nucleation and growth. Dendrite growth at the edge of pits leads to an insufficient supply of Li ions in the pit, thereby inhibiting dendrite formation inside the pit. By optimization of the pattern structure, the density of Li deposition can be significantly improved. The results provide a promising and practical approach to ensure safety and extend the lifetime of Li metal batteries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".