Stable Silicon Anodes by Molecular Layer Deposited Artificial Zincone Coatings
Bibliographic record
Abstract
Abstract A stable interface between silicon anodes and electrolytes is vital to realizing reversible electrochemistry cycling for lithium‐ion batteries. Herein, a zincone polymer coating is controllably deposited on a silicon electrode using the molecular layer deposition to serve as an artificial solid electrolyte interphase (SEI). Enhanced electrochemical cycling depends on the thickness of zincone coating. The optimal zincone coating of ≈3 nm markedly improves the lithium storage performance of silicon anodes, resulting in a high reversible capacity (1741 mA h g−1 after 100 cycles at 200 mA g−1), outstanding cycling stability (1011 mA h g−1 after 500 cycles), and superior rate capability (1580 mA h g−1 at 2 A g−1). Such remarkable electrochemical reversibility stems from the in situ conversion of the zincone coating and a zincone‐driven thin lithium fluoride (LiF)‐rich SEI, which endow the silicon electrode with superior electron/ion transport and structural stability. Meanwhile, the zincone coating demonstrates good compatibility with ether‐based electrolytes (893 mA h g−1 after 200 cycles, 970 mA h g−1 at 5 A g−1). Additionally, in situ conversion of artificial zincone coating also opens a door for constructing a functional interface on other electrode surfaces, such as lithium/sodium metal.
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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".