Highly Stable and Efficient Catalyst with In Situ Exsolved Fe–Ni Alloy Nanospheres Socketed on an Oxygen Deficient Perovskite for Direct CO<sub>2</sub> Electrolysis
Bibliographic record
Abstract
The massive emission of carbon dioxide (CO 2 ), the major portion of greenhouse gases, has negatively affected our ecosystem. Developing new technologies to effectively reduce CO 2 emission or convert CO 2 to useful products has never been more imperative. In response to this challenge, we herein developed novel in situ exsolved Fe–Ni alloy nanospheres uniformly socketed on an oxygen-deficient perovskite [La(Sr)Fe(Ni)] as a highly stable and efficient catalyst for the effective conversion of CO 2 to carbon monoxide (CO) in a high-temperature solid oxide electrolysis cell (HT-SOEC). The symmetry between the reduction and reoxidation cycles of this catalyst indicates its good redox reversibility. The cathodic reaction kinetics for CO 2 electrolysis is significantly improved with a polarization resistance as low as 0.272 Ω cm 2 . In addition, a remarkably enhanced current density of 1.78 A cm –2, along with a high Faraday efficiency (∼98.8%), was achieved at 1.6 V and 850 °C. Moreover, the potentiostatic stability test of up to 100 h showed that the cell was stable without any noticeable coking in a CO 2 /CO (70:30) flow at an applied potential of 0.6 V (vs OCV) and 850 °C. The increased oxygen vacancies together with the in situ exsolved nanospheres on the perovskite backbone ensures sufficiently active sites and consequently improves the electrochemical performance for the efficient CO 2 conversion. Therefore, this newly developed perovskite can be a promising cathode material for HT-SOEC. More generally, this study points to a new direction to develop highly efficient catalysts in the form of the perovskite oxides with perfectly in situ exsolved metal/bimetal nanospheres.
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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".