Enhanced low‐temperature Selective Catalytic Reduction (SCR) of NO<sub>x</sub> by CuO‐CeO<sub>2</sub>‐MnO<sub>x</sub>/γ‐Al<sub>2</sub>O<sub>3</sub> mixed oxide catalysts
Bibliographic record
Abstract
A series of CuO‐MnOx‐CeO2/γ‐Al2O3 catalysts in different ratios were synthesized by a sol‐gel method with the purpose of improving the low‐temperature denitration performance (loading a transition mental oxide (MnOx, CeO2) on a CuO/γ‐Al2O3 copper‐based catalyst). The denitration performance of a low‐temperature SCR under the condition of simulated flue gas was measured using the programmed heating method in the catalytic reaction efficiency evaluation system. The denitration efficiency of the 6 % CuO‐5 % MnOx‐10 % CeO2/γ‐Al2O3 catalytic particles was maintained at over 80 % within a temperature range of 100–200 °C. The catalysts were characterized by surface area analysis (BET), x‐ray diffraction (XRD), and scanning electron microscopy (SEM). The best surface structure characteristics include the 5 % MnOx + 10 % CeO2 loading capacity of the catalyst, which was indicated by a BET analysis. The catalyst surface structure characteristics were effectively promoted by the amount of CeO2 and MnO2 loading proved by the SEM analysis. The possible mechanisms involved in SCR denitration at a low temperature were also discussed. The experimental results revealed that the catalyst granule with perfect surface characteristics and pore features was successfully synthesized by the sol‐gel method. The denitration performances were restrained by 10 % of H2O and 800 mg · m−3 of SO2, indicating that SO2 and H2O have an inhibiting effect on NOx conversion.
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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".