A Novel Organic “Polyurea” Thin Film for Ultralong‐Life Lithium‐Metal Anodes via Molecular‐Layer Deposition
Bibliographic record
Abstract
Abstract Metallic Li is considered as one of the most promising anode materials for next‐generation batteries due to its high theoretical capacity and low electrochemical potential. However, its commercialization has been impeded by the severe safety issues associated with Li‐dendrite growth. Non‐uniform Li‐ion flux on the Li‐metal surface and the formation of unstable solid electrolyte interphase (SEI) during the Li plating/stripping process lead to the growth of dendritic and mossy Li structures that deteriorate the cycling performance and can cause short‐circuits. Herein, an ultrathin polymer film of “polyurea” as an artificial SEI layer for Li‐metal anodes via molecular‐layer deposition (MLD) is reported. Abundant polar groups in polyurea can redistribute the Li‐ion flux and lead to a uniform plating/stripping process. As a result, the dendritic Li growth during cycling is efficiently suppressed and the life span is significantly prolonged (three times longer than bare Li at a current density of 3 mA cm −2 ). Moreover, the detailed surface and interfacial chemistry of Li metal are studied comprehensively. This work provides deep insights into the design of artificial SEI coatings for Li metal and progress toward realizing next‐generation Li‐metal batteries.
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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) | 0.001 | 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".