Effect of Zirconia on Pd–Pt Supported SBA-15 Catalysts for the Oxidation of Methane
Bibliographic record
Abstract
A series of methane oxidation catalysts were prepared by doping Santa Barbara Amorphous-15 (SBA-15), a highly mesoporous silica sieve, with varying amounts of Zr (5, 10, and 15 wt%) and loading with 2 wt% Pd and 4 wt% Pt. The catalysts were characterized using various techniques, including BET, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and H2-temperature programmed reduction (H2-TPR). Fresh and aged catalysts were evaluated for methane oxidation. Aging was performed using a simulated lean burn natural gas (NG) engine exhaust containing water vapor (10% vol) and sulfur (10 ppm). It was found that the catalyst with 15 wt% zirconia was the most active and stable of the series, exhibiting the lowest T50 of 481 °C after 40 h of aging. The Pd–Pt catalyst loaded on pure SBA-15 had a T50 of 583 °C after aging, which was 102 °C higher than that of the Pd–Pt catalyst with 15 wt% Zr. The results suggest that the increased performance was due to the higher amount of reducible PtOx species in the proximity of ZrO2 and the sulfur scavenging effect of zirconia, which protected the active metals from forming inactive sulfur complexes. Overall, the Pd–Pt catalyst with 15 wt% Zr loaded on SBA-15 demonstrated excellent methane oxidation activity, hydrothermal stability, and sulfur resistance and can be considered a viable candidate for reducing the methane slip from a lean burn NG engine exhaust.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".