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 H 2 and CO. The LSFCr (La 0.3 Sr 0.7 Fe 0.7 Cr 0.3 O 3 ) 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 CO 2 conversion back to CO. Many surface chemical reactions were studied for different perovskites, but the CO 2 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 CO 2 electrolysis is unknown. This study identifies among many adsorption modes for CO 2 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 CO 2 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 CO 2 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 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".