State-of-Art Review of NO Reduction Technologies by CO, CH4 and H2
Bibliographic record
Abstract
Removal of nitrogen oxides during coal combustion is a subject of great concerns. The present study reviews the state-of-art catalysts for NO reduction by CO, CH4, and H2. In terms of NO reduction by CO and CH4, it focuses on the preparation methodologies and catalytic properties of noble metal catalysts and non-noble metal catalysts. In the technology of NO removal by H2, the NO removal performance of the noble metal catalyst is mainly discussed from the traditional carrier and the new carrier, such as Al2O3, ZSM-5, OMS-2, MOFs, perovskite oxide, etc. By adopting new preparation methodologies and introducing the secondary metal component, the catalysts supported by a traditional carrier could achieve a much higher activity. New carrier for catalyst design seems a promising aspect for improving the catalyst performance, i.e., catalytic activity and stability, in future. Moreover, mechanisms of catalytic NO reduction by these three agents are discussed in-depth. Through the critical review, it is found that the adsorption of NOx and the decomposition of NO are key steps in NO removal by CO, and the activation of the C-H bond in CH4 and H-H bonds in H2 serves as a rate determining step of the reaction of NO removal by CH4 and H2, respectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".