Unraveling the Structure and Role of Mn and Ce for Selective Catalytic Reduction of NOx in Application-Relevant Catalysts and Conditions
Bibliographic record
Abstract
Abstract Mn-based oxides are promising catalysts for the selective catalytic reduction (SCR) of NOx with NH3 at temperatures below 200 °C. There is a general agreement that combining Mn with another metal oxide, such as CeOx improves catalytic activity. However, to date, there is an unsettling debate on the role of Ce as Mn promoter on the SCR reaction. To solve this, here we have systematically studied the effect Ce by preparing, characterizing and testing around 30 catalysts aiming for a well-controlled homogeneous dispersion of the metal oxides. Our results show that, at low-temperature SCR conditions, the intrinsic activity of the Mn active sites is not positively affected by Ce species in intimate contact. In fact, the results suggest that Ce is electronically interacting with Mn and decreasing the active-site average activity. To confirm our findings, activities reported in literature were surface-area normalized and the analysis do not support an increase in activity by Ce addition. Therefore, we can unequivocally conclude that the beneficial effect of Ce is textural, increasing catalyst surface area and therefore the total number of active sites. Besides, addition of Ce is increasing N2 selectivity as it suppresses second-step oxidation reactions and thus N2O formation by structurally diluting the MnOx active sites. Therefore, the textural promoting effect still makes Ce an interesting additive for Mn catalysts.
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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 | 0.000 | 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".