Gradiently Sodiated Alucone as an Interfacial Stabilizing Strategy for Solid‐State Na Metal Batteries
Bibliographic record
Abstract
Abstract All‐solid‐state metal batteries (ASSMBs) are attracting much attention due to their cost effectiveness, enhanced safety, room‐temperature performance and high theoretical specific capacity. However, the alkali metal anodes (such as Li and Na) are active enough to react with most solid‐state electrolytes (SSEs), leading to detrimental reactions at the metal–SSE interface. In this work, a molecular layer deposition (MLD) alucone film is employed to stabilize the active Na anode/electrolyte interface in the ASSMBs, limiting the decomposition of the sulfide‐based electrolytes (Na3SbS4 and Na3PS4) and Na dendrite growth. Such a strategy effectively improves the room‐temperature full battery performance as well as cycling stability for over 475 h in Na–Na symmetric cells. The modified interface is further characterized by X‐ray photoelectron spectroscopy (XPS) depth profiling, which provides spatially resolved evidence of the synergistic effect between the dendrite‐suppressed sodiated alucone and the insulating unsodiated alucone. The coupled layers reinforce the protection of the Na metal/electrolyte interface. Therefore, alucone is identified as an effective and bifunctional coating material for the enhancement of the metal/electrolyte interfacial stability, paving the way for rapid development and wide application of high‐energy ASSMBs.
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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) | 0.000 | 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 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".