Mechanism of <scp>NO</scp> removal in selective catalytic reduction based on <scp> γ‐Fe <sub>2</sub> O <sub>3</sub> </scp> catalyst doped with <scp>Mg</scp> element
Bibliographic record
Abstract
Abstract The doping of different elements will make the Fe 2 O 3 catalyst show different catalytic characteristics and improve the activity of the Fe 2 O 3 catalyst in selective catalytic reduction (SCR), mainly by increasing the types of reactive oxygen species and the specific surface area of the catalyst. In this paper, density functional theory (DFT) was used to reveal the reaction path and adsorption behaviour of the Mg‐doped γ‐Fe 2 O 3 catalyst. The results show that the doping of Mg ions can contribute electrons and lead to electron migration on the catalyst surface, which changes the acidity of some sites on the catalyst surface. The adsorption energy of NH 3 is related to the binding sites of N atoms on the catalyst surface, and different adsorption sites will be enhanced or weakened due to Mg doping. NH 2 reacts with NO to form N 2 and H 2 O, so the dehydrogenation of NH 3 to the NH 2 radical is a key step in SCR. With doping, this process becomes more likely to occur. In addition, the activation energy barrier of NH 2 formation in the aerobic environment is lower than that in the anaerobic condition, which contributes to NH 3 dehydrogenation. Therefore, doping Mg on the surface of γ‐Fe 2 O 3 catalyst can improve the catalytic activity of NO removal.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".