Pd catalyst supported on ZrO<sub>2</sub>‐Al<sub>2</sub>O<sub>3</sub> by double‐solvent method for methane oxidation under lean conditions
Bibliographic record
Abstract
Abstract The Pd catalyst supported on the Zr0.5Al0.5O1.75 composite material was prepared by the conventional impregnation and double solvent impregnation method. The effects of preparation method on the catalytic properties of the Pd/Zr0.5Al0.5O1.75 catalyst for methane combustion have been investigated systematically. The measurement was evaluated in a multiple fixed‐bed continuous flow micro‐reactor by passing a gas mixture simulating the exhaust emissions from lean‐burn natural gas vehicles. The as‐prepared catalysts were characterized by CO chemical adsorption, transmission electron microscopy (TEM), hydrogen‐temperature programmed reduction (H2‐TPR),and X‐ray photoelectron spectroscopy (XPS) measurements. The results of TEM and CO chemisorption showed that the introduction of double solvent during the preparation of Pd/Zr0.5Al0.5O1.75 was beneficial for the dispersion of Pd nanoparticles and obtained superior resistance to the sintering of Pd on the surface of Zr0.5Al0.5O1.75. The H2‐TPR measurements demonstrated that the double solvent method increased the reducibility of the Pd catalyst. The XPS results further indicated that more active surface oxygen species also can be formed on the catalyst prepared via double solvent process. Thus, this catalyst exhibited better catalytic performance and hydrothermal aging resistance in the methane oxidation compared to its analogues with the same Pd content prepared by the conventional impregnation method.
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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 | 0.001 | 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".