Beneficial effect of adding γ‐AlOOH to the γ‐Al<sub>2</sub>O<sub>3</sub> washcoat of a PdO catalyst for methane oxidation
Bibliographic record
Abstract
Abstract The beneficial effect of adding γ‐AlOOH to the γ‐Al2O3 washcoat of a ceramic cordierite (2MgO · 2Al2O3 · 5SiO2) monolith, used to support a PdO catalyst, is reported for methane oxidation in the presence of water at low temperature (<500°C). The mini‐monolith (400 cells per square inch (CPI), 1 cm diameter × 2.54 cm length; ~52 cells) was washcoated using a suspension of γ‐Al2O3 plus boehmite (γ‐AlOOH), followed by calcination and then deposition of Pd by wet impregnation. An optimum solid content of 25 wt% in the washcoat suspension was used to obtain a ~25 wt% washcoat on the monolith. The presence of γ‐AlOOH enhanced the thermal and mechanical stability of the washcoat, provided that the γ‐AlOOH content was <8 wt%. Temperature‐programmed methane oxidation (TPO) showed that the addition of γ‐AlOOH to the γ‐Al2O3 washcoat decreased the catalyst activity. However, when H2O (2 vol% and 5 vol%) was present in the feed gas, the γ‐AlOOH improved the catalyst activity and stability. A γ‐AlOOH content of ~5 wt% in the washcoat was determined to provide the highest catalyst activity and stability for CH4 oxidation in the presence of water.
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".