Interface engineering of inorganic solid‐state lithium batteries via atomic and molecular layer deposition
Bibliographic record
Abstract
Abstract Currently, conventional organic liquid electrolytes (OLEs) are the main limiting factor for the next generation of high‐energy lithium batteries. There is growing interest in inorganic solid‐state electrolytes (ISEs). However, ISEs still face various challenges in practical applications, particularly at the interface between ISE and the electrode, which significantly affects the performance of solid‐state batteries (SSBs). In recent decades, atomic and molecular layer deposition (ALD and MLD) techniques, widely used to manipulate interface properties and construct novel electrode structures, have emerged as promising strategies to address the interface challenges faced by ISEs. This review focuses on the latest developments and applications of ALD/MLD technology in SSBs, including interface modification of cathodes and lithium metal anodes. From the perspective of interface strategy mechanism, we present experimental progress and computational simulations related to interface chemistry and electrochemical stability in thermodynamic contents. In addition, this article explores the future direction and prospects for ALD/MLD in dynamic stability engineering of interfaces SSBs. image
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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".