Advanced Material Engineering to Tailor Nucleation and Growth towards Uniform Deposition for Anode‐Less Lithium Metal Batteries
Bibliographic record
Abstract
Anode-less lithium metal batteries (ALMBs), whether employing liquid or solid electrolytes, have significant advantages such as lowered costs and increased energy density over lithium metal batteries (LMBs). Among many issues, dendrite growth and non-uniform plating which results in poor coulombic efficiency are the key issues that viciously decrease the longevity of the ALMBs. As a result, lowering the nucleation barrier and facilitating lithium growth towards uniform plating is even more critical in ALMBs. While extensive reviews have focused to describe strategies to achieve high performance in LMBs and ALMBs, this review focuses on strategies designed to directly facilitate nucleation and growth of dendrite-free ALMBs. The review begins with a discussion of the primary components of ALMBs, followed by a brief theoretical analysis of the nucleation and growth mechanism for ALMBs. The review then emphasizes key examples for each strategy in order to highlight the mechanisms and rationale that facilitate lithium plating. By comparing the structure and mechanisms of key materials, the review discusses their benefits and drawbacks. Finally, major trends and key findings are summarized, as well as an outlook on the scientific and economic gaps in ALMBs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.002 |
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".