Electrode Engineering by Atomic Layer Deposition for Sodium‐Ion Batteries: From Traditional to Advanced Batteries
Bibliographic record
Abstract
Abstract Sodium‐ion batteries (SIBs) have emerged as one of the most promising and competitive energy storage systems due to abundant sodium resources and its environmentally friendly features. However, further improvements in the engineering of the SIB electrode/electrolyte interphase—which directly determines the Na‐ion transfer behavior, material structure stability, and sodiation/desodiation property—are highly recommended to meet the continuously increasing requirements for secondary power sources. Reasonably speaking, to promote SIBs, the advanced and controllable interphase/electrode engineering approach exhibits promise by rationally designing the bulk electrode and generating a well‐defined interphase. Atomic layer deposition (ALD) technology, with atomic‐scale deposition, superior uniformity, excellent conformality, and a self‐limiting nature, is thus expected to address the current challenges facing SIBs in terms of low energy density, limited cycling life, and structural instability, and to promote innovations such as multifunctional electrodes and nanostructured materials for advanced SIBs. This review summarizes and discusses the most recent advancements in the interphase engineering of SIBs by ALD via modifying traditional electrodes and designing advanced electrodes (such as 3D, organic, and protected sodium metal electrodes). Furthermore, based on the recent critical progress and current scientific understanding, future perspectives for the engineering of next‐generation SIB electrodes by ALD can be provided.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".