Atomic Layer Deposition of High‐Capacity Anodes for Next‐Generation Lithium‐Ion Batteries and Beyond
Bibliographic record
Abstract
Electrification has great impacts on our modern society. To electrify future transportation, state‐of‐the‐art lithium‐ion batteries (LIBs) are still not sufficient in multiple aspects including cost, energy density, lifespan, and safety. To this end, next‐generation high‐energy LIBs and beyond are highly regarded. In this regard, high‐capacity anodes are undergoing intensive investigation, such as silicon, SnO2, and lithium metal. However, such anode materials are commonly experiencing large volume changes and related issues, which are reflected on mechanical degradation, capacity fading, low efficiency, and unsatisfactory lifetime. To address these challenges, many technical strategies have been investigated. In the past decade, atomic layer deposition (ALD) has emerged as a new promising technique enabling atomic‐scale surface modification and nanoscale design of high‐capacity anodes for high performance. In this review, recent ALD studies on developing high‐capacity anodes for LIBs and beyond are thoroughly summarized. In addition, ALD strategies and their effectiveness in pursing high‐energy LIBs and beyond are discussed. Particularly, we highlighted the latest advances of ALD for addressing the notorious issues associated with Li metal anodes. It is expected that this work will promote the applications of ALD in new battery systems.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".