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
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it