Reduction of Typical Diesel NOx Emissions by SCR-NH3 Using Metal-Exchanged Natural Zeolite and SBA-15 Catalysts
Bibliographic record
Abstract
In this work, the catalytic performance of clinoptilolite (CLIN) and SBA-15 catalysts, doped with Fe and Cu, was evaluated in the selective catalytic reduction of NO using NH3 as a reducing agent (SCR-NH3). Both Cu-CLIN and Fe-CLIN were obtained by ion-exchange using natural clinoptilolite zeolite originating from the Hrabovec deposit (northeast Slovakia region). Cu-SBA-15 and Fe-SBA-15 were prepared by impregnation into SBA-15 mesoporous synthesized silica. Standard catalytic activity tests were carried out on a bench-scale laboratory apparatus using a reaction mixture of a standard test. GHSV of 48,000 h−1 was adopted based on the space velocity of a real NH3-SCR catalyst for diesel vehicles (100–550 °C). All Cu-doped samples showed better NO conversion values than Fe-doped samples. Clinoptilolite catalysts were more active than those based on SBA-15. Maximum NO conversions of about 96% were observed for Cu-CLIN and Fe-CLIN at 350–400 °C, respectively. Moreover, Fe-CLIN also showed higher stability in the presence of SO2 and water steam at 350 °C. These results demonstrate the potential of metal-doped natural clinoptilolite to be used as cost-effective catalysts applied to the abatement of NOx emissions generated in automotive combustion processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".