The promotion effects of TiO<sub>2</sub> on the selective catalytic reduction of NO<sub>x</sub> with NH<sub>3</sub> over ceo<sub>2</sub>‐WO<sub>3</sub>/ZrO<sub>2</sub>: The catalytic performance and reaction route
Bibliographic record
Abstract
Abstract ZrO2‐TiO2 mixed oxides with different contents of TiO2 were prepared by a co‐precipitation method, which were used as the carrier material of CeO2‐WO3/ZrO2‐TiO2 catalysts. The influence of doping TiO2 on NH3‐SCR activity and reaction path of CeO2‐WO3/ZrO2 was systematically investigated by several techniques, including N2‐physisorption, XRD, H2‐TPR, XPS, NOx‐TPD, NH3‐TPD, and in situ DRIFTS. The activity result suggested that the CeO2‐WO3/ZrO2‐TiO2 with 0.20 g/g TiO2 exhibited the optimal NH3‐SCR performance, its operational temperature window was broadened about 55 °C from 254–445 °C of CeO2‐WO3/ZrO2 to 221–467 °C of CeO2‐WO3/ZrO2‐TiO2. The characterization results indicated that the reducibility of CeO2‐WO3/ZrO2 was obviously improved by adding TiO2 into ZrO2, which could improve the low‐temperature NH3‐SCR activity. Besides, TiO2‐doping enhanced the adsorption and activation of NH3, which played a positive role in NH3‐SCR reaction. Finally, the results of in situ DRIFTS demonstrated that the NH3‐SCR reaction route could be transferred from the Eley‐Rideal mechanism over CeO2‐WO3/ZrO2 to the Langmuir‐Hinshelwood mechanism over CeO2‐WO3/ZrO2‐TiO2 at 250 °C.
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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".