Atomic Layer Deposited Non‐Noble Metal Oxide Catalyst for Sodium–Air Batteries: Tuning the Morphologies and Compositions of Discharge Product
Bibliographic record
Abstract
Catalysts can play a critical role in the development of sodium–air batteries (SABs). Atomic layer deposition (ALD) technology enables rational design and atomic utilization of catalyst by homogenously distributing catalytically active material on a variety of substrates. Here, a novel hierarchical nanostructured Co3O4 is decorated on carbon nanotubes by ALD (CNT@Co3O4) and used as a catalyst for SABs. CNT@Co3O4 demonstrates better performance and longer cycle life than a mechanically mixed CNT/Co3O4 nanocomposite. Well‐dispersed ALD Co3O4 catalyst on CNTs, which serves as functionalized active sites, enables rapid electron exchange and high oxygen reduction/evolution activities. Synchrotron‐based X‐ray analysis including X‐ray absorption near edge structure, extended X‐ray absorption fine structure, and scanning transmission X‐ray microscopy characterization techniques have been employed to elucidate the activity of Co3O4 and to investigate the nanoscale discharge product distribution found in SABs. This analysis reveals that Co3O4 catalyst can promote the electrochemical decomposition of sodium peroxide, superoxide, and carbonates. The role of the catalyst in SABs is clarified and discussed in detail.
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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) | 0.000 | 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".