Reactivity and stability of Co‐Ni/Al<sub>2</sub>O<sub>3</sub> oxygen carrier in multicycle CLC
Bibliographic record
Abstract
Abstract This study deals with the development of a bimetallic Co‐Ni/Al 2 O 3 oxygen carrier suitable for a fluidized bed chemical‐looping combustion process. Temperature programmed characterization shows that the addition of Co enhances the reducibility of the oxygen carrier by influencing the metal‐support interactions helping the formation of reducible nickel species. Reactive characterization of the prepared oxygen carriers in a CREC fluidized riser simulator, using multiple reduction/oxidation cycles, demonstrates that the Co‐Ni/Al 2 O 3 particles display excellent reactivity and stability. The addition of Co in the bimetallic Co‐Ni/Al 2 O 3 influences the state of the surface minimizing the formation of nickel aluminate. The addition of Co also inhibits metal particle agglomeration by maintaining consistent metal dispersion during the cyclic oxidation/reduction processes. A solid‐state kinetics for both reduction and oxidation cycles is established using a clarified Avrami‐Erofeev model at nonisothermal conditions. This random nucleation model describes solid phase changes adequately. The activation energy for Co‐Ni/Al 2 O 3 reduction is found to be significantly lower than the activation energy for the unpromoted Ni/Al 2 O 3 sample, with this observation confirming the positive influence of adding Co on the Ni‐Al 2 O 3 oxygen carrier. © 2007 American Institute of Chemical Engineers AIChE J, 2007
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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 | 0.001 |
| 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 | 0.000 |
| 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".