In Situ X-ray Absorption Spectroscopic Study of Fe@Fe<sub><i>x</i></sub>O<sub><i>y</i></sub>/Pd and Fe@Fe<sub><i>x</i></sub>O<sub><i>y</i></sub>/Cu Nanoparticle Catalysts Prepared by Galvanic Exchange Reactions
Bibliographic record
Abstract
Fe@Fe x O y core–shell nanoparticles have been previously shown to be a versatile support for catalytic metals such as Pd and Cu. However, the resulting structure, metal speciation, and performance of such catalysts in catalytic reactions are still poorly understood. Herein, we synthesize Fe@Fe x O y -supported Pd and Cu nanoparticles by controlling the molar ratios of Fe@Fe x O y nanoparticles to Pd 2+ or Cu 2+ species. Scanning transmission electron microscopy analyses show that Pd or Cu NPs are deposited on the exterior shell of the Fe@Fe x O y nanoparticles. In situ X-ray absorption near-edge structure (XANES) spectra were used to follow the formation processes of Fe@Fe x O y /Pd and Fe@Fe x O y /Cu nanoparticles and the performance of Fe@Fe x O y /Pd nanoparticles for Suzuki–Miyaura cross-coupling reactions. The results show that different molar ratios of Fe@Fe x O y nanoparticles to Pd 2+ or Cu 2+ lead to different morphologies of the resulting supported-NP structures. In situ XANES results show that Fe@Fe x O y nanoparticles can effectively fully reduce Pd or Cu salts over the course of ∼20 min to give small Pd or larger Cu nanoparticles on the surface and can also rereduce oxidized Pd in Suzuki–Miyaura cross-coupling reactions.
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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".