Performance of Low‐temperature SCR of NO with NH<sub>3</sub> over MnO<i>x</i>/Ti‐based catalysts
Bibliographic record
Abstract
ABSTRACT The effects of Mn loadings and precursors, catalyst preparation methods, incineration durations and temperatures, and the addition of Co and Ce on NO‐reduction efficiency and selectivity (N2O formation) during the preparation of MnOx/Ti‐based catalysts were studied by micropore‐size analysis (XRD, XPS, SEM, and FTIR), while considering changeable parameters. Meanwhile, the performance of low‐temperature SCR of NO with NH3 over the designed catalysts was tested under various gas hourly space velocities (GHSVs), NH3/NO molar ratios, and contents of NO, NH3, O2, H2O, and SO2 in a lab‐scale reactor. Overall, the Mn(0.3)Ce(0.1)/Ti catalyst, which had high NO‐reduction efficiency and selectivity (low N2O formation), was recommended, with the following preparation methods: ultrasonic impregnation; manganese acetate precursor; and incineration at 500 °C. Appropriate textural properties (high surface area and small pore and crystallite sizes), well‐dispersed amorphous manganese (rather than crystalline) on the anatase surface (rather than rutile), abundant active sites, and long residence time are essential for high NO‐reduction efficiency. In practice, NO‐reduction efficiency decreased with increasing GHSV and the NH3 and NO contents; however, it initially increased and then became saturated with an increasing NH3/NO molar ratio and O2 content. Water deactivated the catalyst to a recoverable state, whereas SO2 resulted in unrecoverable deactivation.
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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) | 0.001 | 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".