Chemical looping combustion characteristics and kinetic behaviour of Sr‐doped perovskite‐type <scp>CaFeO<sub>3</sub></scp> oxygen carriers: Theoretical and experimental investigations
Bibliographic record
Abstract
Abstract Oxygen carriers (OCs) with typical perovskite structures have attracted attention for use in chemical looping combustion (CLC) owing to their unique tunable structures and excellent performance. Thus, a further improvement in the reactivity and a deep understanding of the kinetic behaviour in CLC are highly desirable for such perovskite OCs. In this study, a series of Sr‐doped perovskite‐structured CaFeO3 OCs (denoted as SrxCa1−xFeO3) were synthesized. The CLC characteristics, kinetic behaviour, and doping mechanism were systematically investigated via experiments and density functional theory (DFT) calculations. The activation energies of SrxCa1−xFeO3 OCs with various Sr contents were found to be in the range of 36.6–40.1 kJ/mol and lower than that of CaFeO3 (62.7 kJ/mol), indicating that the Sr doping enhanced the reactivity of CaFeO3. Among the OCs, Sr0.4Ca0.6FeO3, which had the lowest activation energy and the fastest release of lattice oxygen, was regarded as the optimum OC. DFT calculations indicated that the reaction energy barrier of SrxCa1−xFeO3 (0.73–1.06 eV) was lower than that of CaFeO3 (2.18 eV). This suggests that Sr doping and the regulation of the reaction pathways are essential drivers for enhancing the reactivity of SrxCa1−xFeO3, which affects the release of lattice oxygen and the morphological properties of OC particles.
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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".