Rational Design of Supported PdAu Nanoparticle Catalysts from Structured Nanoparticle Precursors
Why this work is in the frame
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Bibliographic record
Abstract
A series of poly(vinylpyrrolidone) (PVP)-stabilized metallic and bimetallic PdAu nanoparticles (coreduced and core−shell) with narrow size distributions were encapsulated into alumina matrixes by sol−gel chemistry, and their chemical, structural, electronic, and catalytic behaviors were investigated. Monodisperse nanoparticles were uniformly distributed in the alumina frameworks as observed by TEM images, and single-particle energy-dispersive spectroscopy (EDS) analyses confirmed the high compositional uniformity of the bimetallic nanoparticles. A combination of TEM, EDS mapping, TGA, XANES and EXAFS studies were used to fully characterize the alumina-supported nanoparticles before and after thermal treatments. It was observed that the size distribution of the final PdAu nanoparticles was highly dependent on calcination conditions, and careful high-temperature calcinations at 300 °C could be used to remove organic PVP stabilizers with minimal particle aggregation and/or structural transformations. The resulting supported nanoparticle catalysts were found to be active as hydrogenation catalysts. EXAFS analysis of coreduced PdAu nanoparticles indicated they had near-alloy structures with slightly Au-rich cores and Pd-rich shells before and after calcination, while intentionally designed Pd-core Au-shell nanoparticles retained their structures after calcination. XANES spectra of both coreduced and core−shell PdAu nanoparticles were also examined and showed that the PdAu coreduced nanoparticles had fewer Au valence d-band vacancies in comparison to monometallic nanoparticles while the PdAu core−shell nanoparticles had relatively higher Au valence d-band vacancies than the coreduced PdAu nanoparticles.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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 it