Enhanced Stability and Thickness‐Independent Oxygen Evolution Electrocatalysis of Heterostructured Anodes with Buried Epitaxial Bilayers
Bibliographic record
Abstract
Abstract Achieving high oxygen evolution reaction (OER) activity while maintaining performance stability is a key challenge for designing perovskite structure oxide OER catalysts, which are often unstable in alkaline environments transforming into an amorphous phase. While the chemical and structural transformation occurring during electrolysis at the electrolyte–catalyst interface is now regarded as a crucial factor influencing OER activity, here, using La 0.7 Sr 0.3 CoO 3− δ (LSCO) as an active OER catalyst, the critical influence of buried layers on the oxidation current stability in nanoscopically thin, chemically and structurally evolving, catalyst layers is revealed. The use of epitaxial thin films is demonstrated to engineer both depletion layer widths and chemical stability of the catalyst support structure resulting in heterostructured anodes that maintain facile transport kinetics across the electrolyte–anode interface for atomically thin (2–3 unit cells) LSCO catalyst layers and greatly enhanced oxidation current stability as the perovskite structure OER catalysts chemically and structurally transform. This work opens up an approach to design robust and active heterostructured anodes with dynamically evolving ultrathin OER electrocatalyst layers for future green fuel technologies such as conformal coatings of high‐density 3D anode topologies for water splitting.
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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".