Optimal Oxygen Vacancy Concentration for CO<sub>2</sub> Reduction in LSFCr Perovskite: A Combined Density Functional Theory and Thermogravimetric Analysis Measurement Study
Bibliographic record
Abstract
A solid oxide fuel cell (SOFC) plays a significant role in converting chemically stored energy to electrical energy by using clean and renewable fuels, such as H2 and CO. The LSFCr (La0.3Sr0.7Fe0.7Cr0.3O3) perovskite is one of the few materials that is especially efficient and stable as a reversible SOFC (RSOFC) by performing not only the direct fuel cell reaction to generate power but also the CO2 conversion back to CO. Many surface chemical reactions were studied for different perovskites, but the CO2 reduction to CO at gaseous conditions has been reported for only a few materials. Unfortunately, for the LSFCr perovskite the precise atomic structures during mechanism of CO2 electrolysis is unknown. This study identifies among many adsorption modes for CO2 on the LSFCr surface the preferred active site and a suggested mechanism for the reaction using density functional theory (DFT) with nudged elastic band (NEB) tools. Surprisingly, the mechanism involves a stable, linear O–C–O angle during adsorption of CO2 and bending of the angle is achieved only during the transition state. The results demonstrate the importance of oxygen vacancies in the catalytic process, as well as the importance of a Cr dopant in the reduction despite the direct bonding of CO2 to Fe atom. Our results on the necessity of a particular oxygen vacancy concentration for the chemical reaction is supported by our thermogravimetric analysis (TGA) measurement.
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How this classification was reachedexpand
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 | 0.002 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".