Reduction Kinetics of Perovskite-Based Oxygen Carriers for Chemical Looping Combustion
Bibliographic record
Abstract
LaMnO 3 and LaMnO 3 @mSiO 2 oxygen carriers were studied in a mini-fluidized bed reactor for chemical looping combustion, and their reduction behavior was first simulated using the following two classical kinetic models: the nucleation and nuclei growth model (NNGM) and the unreacted shrinking-core model. Furthermore, another model designated as the oxygen ions diffusion model was derived by assuming oxygen ion diffusion as the rate-limiting step for the reduction of both oxygen carriers. These materials were characterized using XRD for phase identification, BET for determination of specific surface area, pore volume and pore size distribution, SEM and TEM for morphology, particle size and shell thickness determination, H 2 -TPR and TPD-O 2 for oxido–reduction properties analysis. H 2 -TPR results of LaMnO 3 @mSiO 2 core–shell structured material indicated that the reduction of Mn 3+ into Mn 2+ is more difficult than in LaMnO 3 as the low-temperature reduction peak was shifted to a higher temperature. Both carriers exhibited high reactivity toward methane combustion during repeated reduction–oxidation cycles. It was found that both NNGM and the shrinking-core model yielded poor fits of the kinetic data. The determined activation energies ( E a ) by NNGM for reduction of LaMnO 3 and LaMnO 3 @mSiO 2 are 23 and 57 kJ/mol, respectively. The higher value of E a for LaMnO 3 @mSiO 2 reflects the increased difficulty of perovskite phase reduction which was also indicated by H 2 -TPR results. This result was also observed using the oxygen ions diffusion model (diffusivity activation energy was found higher for LaMnO 3 @mSiO 2 ). The diffusion coefficients of oxygen obtained as adjustable parameters in this case show realistic values, thus adding credibility to the oxygen ions diffusion model.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".