Kinetic and deactivation studies of methane oxidation over palladium catalysts in the presence of water
Bibliographic record
Abstract
Natural gas vehicles (NGVs) generate considerably fewer emissions of CO, NOx and CO₂ in comparison to conventional diesel and gasoline vehicles, although these benefits are mitigated by the presence of significant amounts of CH₄ in the exhaust. The relatively low temperature (423–823 K) and high concentrations of CO₂ and H₂O in the NGV exhaust gas make current catalytic converters inefficient for the removal of unburned CH₄. Although Pd is the most active metal for CH₄ oxidation, Pd catalysts deactivate after long time exposure to the NGV exhaust conditions. This thesis develops an understanding of the deactivation mechanisms of Pd supported catalysts following thermal treatments and examines the kinetics of CH₄ oxidation, accounting for the effect of H₂O. Hydrothermal aging (HTA) at high temperatures (673–973 K) is shown to significantly deactivate PdO/SiO₂ catalysts used for CH₄ oxidation. PdO occlusion by the SiO₂ support is responsible for catalyst deactivation at low HTA temperatures (673 K), whereas a combination of PdO sintering and PdO occlusion contributes to significant deactivation at high HTA temperatures (973 K). The stability of PdO catalysts during HTA is dependent upon the support. PdO/α-Al₂O₃ is found to have the highest catalyst stability during HTA at 973 K for up to 65 h and its high stability is attributed to a strong Pd-support interaction. Although PdO crystallites sinter and are occluded by the support during HTA, PdO occlusion only affects PdO/SiO₂ performance significantly. The deactivation of PdO/γ-Al₂O₃ and PdO/SnO₂ during HTA at 973 K is attributed to PdO sintering and a PdO → Pd⁰ transformation. The kinetics of CH₄ oxidation over a PdO/γ-Al₂O₃ catalyst is also reported. A power law model can accurately predict the observed temperature-programmed CH₄ oxidation data profiles measured for PdO/γ-Al₂O₃ at conversions < 40% when 0.1 v.% CH₄ with no H₂O or 0.5 v.% CH₄ with ≤ 2 v.% H₂O are present in the feed. At high H₂O concentration, model fit is improved with 0.1 v.% CH₄ in the feed but shows significant deviation with 0.5 v.% CH₄ in the feed.
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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 | 0.001 |
| 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".