Modification of KNO<sub>3</sub> on the reducibility and reactivity of Fe<sub>2</sub>O<sub>3</sub>‐based oxygen carriers for chemical‐looping combustion of methane
Bibliographic record
Abstract
This work presented a comparative study on the Fe2O3‐based oxygen carriers modified by different potassium salts (i.e. KNO3, K3PO4, KOH, K2CO3) for chemical looping combustion of methane (CLC with methane). The effect of Al2O3 support on the reactivity of Fe2O3 was also investigated. The Fe2O3‐based oxygen carriers were prepared by a co‐precipitation method and the potassium salts were added via an incipient wetness impregnation method. The obtained oxygen carriers were characterized by XRD, XPS, H2‐TPR, and CH4‐TPR technologies. It was found that the presence of KNO3 could improve the reducibility of the Fe2O3‐based oxygen carrier in the surface, resulting in relatively high selectivity towards CO2 during the CLC with methane. The content of KNO3 also affected the reactivity of oxygen carriers, and 10 % KNO3/Fe2O3 sample showed the best activity. Moreover, it was also demonstrated that the addition of Al2O3 support into the 10 % KNO3/Fe2O3 oxygen carrier could improve its redox performance, and the stability of 10 % KNO3/Fe2O3/Al2O3 was proved to be favourable after twenty successive cycles for CLC with methane.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".