Isotopic Oxygen Exchange Study to Unravel Noble Metal Oxide/Support Interactions: The Case of RuO<sub>2</sub> and IrO<sub>2</sub> Nanoparticles Supported on CeO<sub>2</sub>, TiO<sub>2</sub> and YSZ
Bibliographic record
Abstract
Abstract The aim of this study is to unravel the mechanism of (noble metal oxide)/(active support) interactions for catalytic purposes. Hence, isotopic oxygen exchange (IOE) tests were performed on Iridium‐ and ruthenium‐based oxides supported on cerium oxide (CeO2), titanium oxide (TiO2), and yttria‐stabilized zirconia (YSZ). IOE tests demonstrated the metal oxide support involvement in the propane oxidation reaction, with YSZ‐based catalysts showing the highest exchange rate of oxygen, while CeO2 and TiO2‐based catalysts had a less diffusion of lattice oxygen in that order. This is related to the presence of extrinsic oxygen vacancies in the YSZ‐based catalysts and the reduction ability of the CeO2 and TiO2 supports. Despite the limitations on oxygen exchange in some of the noble/metal oxide catalysts, their catalytic performance was comparable to the ones that showed a high oxygen exchange. Therefore, active supports results in a higher engagement of oxygen from the support but not in a linear correlation with the catalytic performance of the metal/support.
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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".