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 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".