Copper Doping Promotion on Ce/CAC-CNT Catalysts with High Sulfur Dioxide Tolerance for Low-Temperature NH<sub>3</sub>–SCR
Bibliographic record
Abstract
The stumbling block to the ever-increasing need for improving air quality remains nitrogen oxides (NO x ). The copper-introduced Ce/CAC-CNT (Cu x Ce/CAC-CNTs) catalyst using the in situ-growth-prepared activated carbon and carbon-nanotube composite (CAC-CNT) carrier with high sulfur dioxide tolerance was successfully applied to low-temperature NH 3 –SCR in this study. The findings indicate that the Cu x Ce/CAC-CNTs obtained at a 0.2 Cu/Ce molar ratio and the calcination temperature of 450 °C showed the highest 100% NO conversion with 95.8% N 2 selectivity at 150 °C and 10 000 h –1 . The incorporation of Cu improved the Cu 0.2 Ce/CAC-CNTs in Lewis acid, lattice oxygen (31.99%), and Ce 3+ (26.03%). The accelerated NH 3 adsorption on acid sites, the encouraging electron transfer by the Ce 4+ + Cu + ↔ Ce 3+ + Cu 2+ redox circle, and the more surface chemisorbed oxygen (O β ) improved the catalytic activity of Cu 0.2 Ce/CAC-CNTs. The NH 3 –SCR of Cu 0.2 Ce/CAC-CNTs largely follows the L–H mechanism, together with a certain degree of “Fast SCR.” The added Cu species effectively prevented surface SO 2 adsorption and oxidation, and the Cu 0.2 Ce/CAC-CNTs restored more than 94% SCR activity after 8 h of poisoning in 50 ppm SO 2 and 5 vol % H 2 O.
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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".