Investigation of Heterogeneous Catalysts by an Electrochemical Method: Ceria and Titania-Supported Iridium Nanoparticles for Ethylene Oxidation
Bibliographic record
Abstract
Recently, metal support interaction (MSI) has been demonstrated to be closely related to electrochemical promotion of catalysis (EPOC) in the functionality of the process through spillover/backspillover of ionic species to/from the conductive support. In the present work, the interaction that iridium oxide (IrOx) nanoparticles have with two mixed ionic-electronic conducting (MIEC) materials (i.e., ceria, CeO2 and titania, TiO2) for ethylene oxidation is evaluated. To this end, the open circuit catalytic oxidation of ethylene as well as steady-state polarization measurements were carried out for free-standing (unsupported) IrOx and ceria- and titania-supported IrOx (~1 nm). The presence of these two supports was found to increase the catalytic reaction rate when compared to the free-standing IrOx, and decrease the electrochemical reaction rate at the three-phase boundary, as confirmed by the exchange current density (i0). In the light-off experiments, the IrOx/CeO2 catalyst showed a higher reaction rate until 3000C; however IrOx/TiO2 was superior at 3500C. It was also shown that the catalysts with lower i0 resulted in higher open-circuit reaction rates due to the larger amount of thermally-induced backspillover promoters to the gas exposed catalyst surface, in agreement with the EPOC phenomenon.
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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) | 0.000 | 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".