Local Structure, Electronic Behavior, and Electrocatalytic Reactivity of CO-Reduced Platinum–Iron Oxide Nanoparticles
Bibliographic record
Abstract
A series of platinum–iron oxide nanoparticles was synthesized using a “clean” CO-reduction method that employed different ratios of Pt−Fe precursor salts in oleylamine at elevated temperatures. High-resolution transmission electron microscopy (HRTEM) and energy-dispersive X-ray spectroscopy (EDS) studies revealed that nearly monodisperse (i.e., with relative standard deviations of less than 15%) nanoparticles with mean diameters of 3.5–4.4 nm and varied elemental compositions (Pt 54 Fe 46 Pt 70 Fe 30, and Pt 87 Fe 13 ) were obtained. X-ray absorption near-edge structure (XANES) and extended X-ray absorption fine structure (EXAFS) measurements at the Pt L 3 - and Fe K-edges revealed that these nanoparticles all consisted of a Pt core with amorphous iron oxide on the surface. Furthermore, it was observed that the local structure (e.g., Pt–Pt bond distance and coordination number) and electronic behavior of the Pt–FeO nanoparticles (e.g., Pt d electron density and Fe valence state) are dependent on the Pt−Fe precursor ratios used in their synthesis. Quantum mechanical ab initio calculations were employed to interpret the results from X-ray spectroscopy and help elucidate the relationships between local structure and electronic properties in the nanoparticle samples. Finally, the surface reactivity of these nanoparticles in the oxygen reduction reaction (ORR) was explored, demonstrating higher electrocatalytic activity for all three platinum–iron oxide samples in comparison with a commercial Pt catalyst. The surface reactivity was also found to be sensitive to the Pt−Fe ratios of the nanoparticles and could be correlated with their local structure and electronic behavior.
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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".