Insights into Syngas Combustion on a Defective NiO Surface for Chemical Looping Combustion: Oxygen Migration and Vacancy Effects
Bibliographic record
Abstract
A thorough theoretical analysis of vacancy effects on syngas combustion was conducted for the chemical looping combustion (CLC) process using NiO as an oxygen carrier (OC). A density functional theory (DFT) analysis was conducted to provide new insights into the effects of surface vacancies on OC behavior such as O migration, syngas adsorption, and syngas oxidation. The results showed that the vacancies are expected to promote the subsequent oxidation reactions, which might come from the lower state energies of the reactants, the syngas adsorption configurations on the defective surface. Furthermore, the proposed reaction mechanisms showed that the presence of the defective sites benefited the syngas oxidation by reducing the energy barriers of the CO and H 2 oxidation reactions. In particular, H 2 oxidation changed from a 3-step process on a perfect surface (i.e., without vacancies) to a 2-step process on a defective surface. Moreover, the CO oxidation reaction was shown to dominate the overall syngas oxidation process. In addition, the outward diffusion direction of oxygen migration was observed from the bulk side to the surface. The proposed CO and H 2 reaction kinetics were validated against the experimental data using a DFT-based mean-field (MF) model. An electronic analysis was also performed to further support the intrinsic effects of surface vacancies obtained from the DFT analysis.
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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 | 0.001 |
| Open science | 0.001 | 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".