Catalytic and electrochemical evaluation of the role of metal oxides on Pd nano-catalysts for complete methane oxidation
Bibliographic record
Abstract
• Adding metal oxide (SnO 2 and ZnO) to Pd nanoparticles increased methane oxidation rate. • Addition of FeOx to Pd inhibits the reaction in oxidizing and strongly promotes in reducing conditions. • Electrochemical methods applied to study Pd-MOx oxidation state. • An opposite relationship between the catalytic rate of Pd-MOx and electrochemical response. Catalytic complete methane oxidation over Pd-MOx (MO x = SnO 2 , FeO x , and ZnO) nanoparticles deposited on YSZ solid electrolyte was evaluated for and compared to a monometallic Pd catalyst. To this end, the nanoparticles were synthesized via the polyol method and tested for methane oxidation in a temperature range from 200 to 475 °C under reducing, stoichiometric, and oxidizing reaction conditions in open-circuit conditions. The light-off experiments revealed that the presence of a second phase in the form of metal oxide (SnO 2 and ZnO) increased the catalytic rate of the reaction compared to monometallic palladium in all gas compositions. However, the addition of iron oxide to Pd showed a different behaviour, i.e., a strong inhibition of the reaction rate in the oxidizing and stoichiometric conditions and significant promotion in the reducing conditions. To gain an insight into the role of MO x in various conditions, the detailed electrochemical measurements were carried out at selected temperatures. The exchange current density ( i o ) of the electrochemical process at the three phase bpoundary was found to depend on the oxidation state of the catalyst, which in turn influences the catalytic rate of Pd-MO x . Overall, the addition of the cheaper oxide to Pd significantly promotes the catalytic reaction, and the inverse relation between the catalytic rate and i o was found in agreement with the electrochemical promotion of catalysis (EPOC) mechanism, where the lower exchange current density values correspond to the higher catalytic reaction rate of complete methane oxidaiton.
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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) | 0.001 | 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".