Stabilizing Perovskite Phase via A‐Site Charge Density Modulation During Metal In Situ Exsolution for Robust CO <sub>2</sub> Electrolysis
Bibliographic record
Abstract
Abstract Perovskite in situ exsolution is an effective approach for fabricating robust heterostructures with superior catalytic properties, but the concomitant phase transformation occurring in parent perovskite matrix often cause compromised structural integrity and diminished catalytic activity. Here a series of high‐charge density Ca‐doped Sr 2− x Ca x Fe 1.3 Ni 0.2 Mo 0.5 O 6−δ (Ca x SFNM, x ≤ 0.5) is synthesized, and treated them in reducing atmospheres to in situ exsolve FeNi 3 nanoalloys (FeNi 3 @Ca x SFNM). The phase structure progressively evolves during exsolution as Ca content decreases, among which FeNi 3 @Ca 0.5 SFNM preserves its double perovskite structure with maximal oxygen vacancy concentration, whereas other counterparts exhibit stepwise structural reconstruction. Moreover, increased oxygen vacancies strengthen their surface interactions with CO 2 , conferring FeNi 3 @Ca 0.5 SFNM with exceptional CO 2 electrolysis performance, where a current density of 1.05 A cm −2 and a CO Faraday efficiency of 95.38%, coupled with a minimal decay rate of only 0.8 mA cm −2 h −1 during 200 h of test, are obtained at 850 °C and 1.5 V, surpassing others with varying phase transitions. Theoretical calculations reveal that relative to Sr 2+ , Ca 2+ enhances electronic coupling of A‐O‐B sites and B 3d‐O 2p orbital hybridization, ultimately reinforcing B─O bond covalency to suppress phase transition and oxygen vacancy loss upon exsolution.
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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) | 0.001 | 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".