Fluorine-Stabilized BO<sub>6</sub> Octahedron of Host Perovskites for Robust Carbon Dioxide Electrolysis on Exsolved Catalysts
Bibliographic record
Abstract
Exsolution of nanoparticles has become a prevalent technique for enhancing the catalytic activity of perovskites for carbon dioxide (CO 2 ) electrolysis in solid oxide electrolysis cells. However, the potential negative impact of phase evolution of the host perovskite on catalytic performance is often overlooked in light of the overall performance enhancement from exsolution. Herein, we illustrate a facile fluorine doping strategy to suppress the phase transition of Sr 2 Fe 1.2 Ni 0.3 Mo 0.5 O 6 –δ (SFN 3 M) during exsolution. The experimental characterizations combined with density functional theory calculations reveal that the incorporation of fluorine into the SFN 3 M lattice is beneficial for preserving the high oxidation states of B-site cations and inhibiting the lattice oxygen loss, resulting in a robust BO 6 octahedron in the host perovskite. It is found that the well-preserved double perovskite structure exhibits a stronger interaction with CO 2, thus enhancing the catalytic activity of F-doped exsolved SFN 3 M (F-SFN 3 M-red). Furthermore, the robust BO 6 octahedron of the host perovskite significantly enhances the resistance of F-SFN 3 M-red to decomposition under high-voltage CO 2 electrolysis, leading to the significantly increased carbon monoxide productivity over a broad voltage range. These findings highlight that the F doping strategy has great potential to aid the development of exsolved perovskites with high catalytic activity and stability for a wider range of electrocatalysis applications.
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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".