NO reduction by CO over iron‐based catalysts supported by activated semi‐coke
Bibliographic record
Abstract
Activated semi‐coke was developed and loaded by iron species and other assistant metals (Co, La, and Ce) using a hydrothermal method to obtain the abatement of NOx emissions from power plants and then used for NO removal by CO. These catalysts were systematically characterized by N2 physisorption, scanning electron microscopy, X‐ray diffraction, X‐ray photoelectron spectroscopy, in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), and the activity of NOx reduction. The results showed that the activated semi‐coke that was loaded by Fe species could achieve an excellent NOx conversion of 96.5 % at 350 °C with 0.2 g/g (20 wt%) of the precursor content (catalyst denoted as Fe20/ASC). This was ascribed to higher amounts of surface Fe3+ and chemisorbed oxygen. Furthermore, Co was the best promoter among Co, La, and Ce. This enhancement was attributed to the increase of the specific surface area and chemisorbed oxygen, as well as the formation of CoFe2O4. The synergistic effect between Fe and Co species was beneficial for the formation of oxygen vacancies, which could promote the adsorption and dissociation of NO. The in situ DRIFTS results indicated that the reaction for NOx removal by CO over activated semi‐coke supported catalysts mainly occurred between coordinated nitrates/nitryls and the adsorbed COx species.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".