Growth Mechanism of Micro/Nano Metal Dendrites and Cumulative Strategies for Countering Its Impacts in Metal Ion Batteries: A Review
Bibliographic record
Abstract
Metal-ion batteries are capable of delivering high energy density with a longer lifespan. However, they are subject to several issues limiting their utilization. One critical impediment is the budding and extension of solid protuberances on the anodic surface, which hinders the cell functionalities. These protuberances expand continuously during the cyclic processes, extending through the separator sheath and leading to electrical shorting. The progression of a protrusion relies on a number of in situ and ex situ factors that can be evaluated theoretically through modeling or via laboratory experimentation. However, it is essential to identify the dynamics and mechanism of protrusion outgrowth. This review article explores recent advances in alleviating metal dendrites in battery systems, specifically alkali metals. In detail, we address the challenges associated with battery breakdown, including the underlying mechanism of dendrite generation and swelling. We discuss the feasible solutions to mitigate the dendrites, as well as their pros and cons, highlighting future research directions. It is of great importance to analyze dendrite suppression within a pragmatic framework with synergy in order to discover a unique solution to ensure the viability of present (Li) and future-generation batteries (Na and K) for commercial use.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".