Elegant design of electrode and electrode/electrolyte interface in lithium-ion batteries by atomic layer deposition
Bibliographic record
Abstract
Lithium-ion batteries (LIBs) are very promising power supply systems for a variety of applications, such as electric vehicles, plug-in hybrid electric vehicles, grid energy storage, and microelectronics. However, to realize these practical applications, many challenges need to be addressed in LIBs, such as power and energy density, cycling lifetime, safety, and cost. Atomic layer deposition (ALD) is emerging as a powerful technique for solving these problems due to its exclusive advantages over other film deposition counterparts. In this review, we summarize the state-of-the-art progresses of employing ALD to design novel nanostructured electrode materials and solid-state electrolytes and to tailor electrode/electrolyte interface by surface coatings in order to prevent unfavorable side reactions and achieve optimal performance of the electrode. Insights into the future research and development of the ALD technique for LIB applications are also discussed. We expect that this review article will provide resourceful information to researchers in both fields of LIBs and ALD and also will stimulate more insightful studies of using ALD for the development of next-generation 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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".