Ex Situ Reconstruction-Shaped Ir/CoO/Perovskite Heterojunction for Boosted Water Oxidation Reaction
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The oxygen evolution reaction (OER) is the performance-limiting step in the process of water splitting. In situ electrochemical conditioning could induce surface reconstruction of various OER electrocatalysts, forming reactive sites dynamically but at the expense of fast cation leaching. Therefore, achieving simultaneous improvement in catalytic activity and stability remains a significant challenge. Herein, we used a scalable cation deficiency-driven exsolution approach to ex situ reconstruct a homogeneous-doped cobaltate precursor into an Ir/CoO/perovskite heterojunction (SCI-350), which served as an active and stable OER electrode. The SCI-350 catalyst exhibited a low overpotential of 240 mV at 10 mA cm –2 in 1 M KOH and superior durability in practical electrolysis for over 150 h. The outstanding activity is preliminarily attributed to the exponentially enlarged electrochemical surface area for charge accumulation, increasing from 3.3 to 175.5 mF cm –2 . Moreover, density functional theory calculations combined with advanced spectroscopy and 18 O isotope-labeling experiments evidenced the tripled oxygen exchange kinetics, strengthened metal–oxygen hybridization, and engaged lattice oxygen oxidation for O–O coupling on SCI-350. This work presents a promising and feasible strategy for constructing highly active oxide OER electrocatalysts without sacrificing durability.
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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) | 0.002 | 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".