Emerging Applications of Atomic Layer Deposition for Lithium‐Ion Battery Studies
Bibliographic record
Abstract
Lithium-ion batteries (LIBs) are used widely in today's consumer electronics and offer great potential for hybrid electric vehicles (HEVs), plug-in HEVs, pure EVs, and also in smart grids as future energy-storage devices. However, many challenges must be addressed before these future applications of LIBs are realized, such as the energy and power density of LIBs, their cycle and calendar life, safety characteristics, and costs. Recently, a technique called atomic layer deposition (ALD) attracted great interest as a novel tool and approach for resolving these issues. In this article, recent advances in using ALD for LIB studies are thoroughly reviewed, covering two technical routes: 1) ALD for designing and synthesizing new LIB components, i.e., anodes, cathodes, and solid electrolytes, and; 2) ALD used in modifying electrode properties via surface coating. This review will hopefully stimulate more extensive and insightful studies on using ALD for developing high-performance LIBs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".