Nanoengineered Ir<sub>core</sub>@Pt<sub>shell</sub> Nanoparticles with Controlled Pt Shell Coverages for Direct Methanol Electro-Oxidation
Bibliographic record
Abstract
The design and application of bimetallic alloy nanoparticles (NPs) for electrocatalytic applications are challenged by the need to clearly identify and understand the individual effect of each component. In the present work, the focus has been on PtIr NPs, with alloyed NPs being previously shown to be active toward the methanol oxidation reaction (MOR), but for which the mode of action of the Ir component remains uncertain. We have therefore nanoengineered a family of Ir core @Pt shell NPs, using a modified polyol method, to control the Pt shell coverage (up to 2 monolayers) and thus to allow the separation of the bifunctional and electronic effects of Ir on the Pt activity. It is shown that the Ir core size and crystallinity do not change with the deposition of the Pt shell, as confirmed by transmission electron microscopy and X-ray diffraction. CO stripping and hydrogen underpotential deposition/removal were used for the first time to determine the surface composition of the Ir core @Pt shell NPs. It is shown that the Ir core enhances the MOR activity of the Pt shell primarily through the bifunctional effect, with an optimum Pt coverage of 0.4 of a monolayer. At 60 °C, an additional electronic effect of Ir on Pt can be discerned, causing an inhibition in the MOR rate by weakening the adsorption of methanol on the Pt shell, thus helping to remove the adsorbed CO intermediate from the shell surface.
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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".