Lithium dendrites in all‐solid‐state batteries: From formation to suppression
Bibliographic record
Abstract
Abstract All‐solid‐state lithium (Li) metal batteries combine high power density with robust security, making them one of the strong competitors for the next generation of battery technology. By replacing the flammable and volatile electrolytes commonly found in traditional Li‐ion batteries (LIBs) with noncombustible solid‐state electrolytes (SSEs), we have the potential to fundamentally enhance safety measures. Concurrently, SSE would be capable of fitting high specific capacity (3860 mAh g −1 ) metal Li and is expected to break through the upper limit of mass‐energy density (350 Wh kg −1 ) of existing LIBs system. Nevertheless, the growth of Li dendrites on the negative side or the nucleation of Li inside SSEs may give rise to battery short circuits, which is the primary factor limiting the application of Li metal. Recognizing this, the focus of this review is to provide a perspective for experimentalists and theorists who closely monitor various surface/interface and microstructure phenomena to understand Li dendrites. The strategies to reveal the complicated deposition mechanism and to control the dendrite growth of metal Li in solid‐state batteries, as well as the advanced characterization methods of metal Li, provide suggestions for the practical research of solid‐state 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 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) | 0.000 | 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) | 0.000 | 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".