General Method for Determination of the Surface Composition in Bimetallic Nanoparticle Catalysts from the L Edge X-ray Absorption Near-Edge Spectra
Bibliographic record
Abstract
Bimetallic PtPd on silica nanoparticle catalysts have been synthesized, and their average structure has been determined by Pt L 3 and Pd K edge extended X-ray absorption fine-structure spectroscopy. The bimetallic structure is confirmed from elemental line scans by scanning transmission electron microscopy of the individual 2-nm-sized particles. A general method is described to determine the surface composition of bimetallic nanoparticles even when both metals adsorb; for example, CO, by combining the quantitative characterization by X-ray absorption near-edge structure spectra at L edges with CO adsorption with the adsorption stoichiometry determined by Fourier transform infrared spectroscopy. Determination of the surface composition leads to a better understanding of the changes in catalytic chemistry that accompany alloy formation. Although monometallic Pt and Pd have similar turnover rates for neopentane hydrogenolysis and isomerization, on the basis of the surface composition, it appears that in the bimetallic PtPd catalysts, the rate and products are determined predominantly by Pt with little contribution from surface Pd. Density functional theory calculations indicate that the center of the Pt d-band density of states shifts to higher energy, or closer to the Fermi level, whereas that in Pd shifts to lower energy away from the Fermi level. Similarly, the calculated enthalpy of CO adsorption increases on Pt, but decreases on Pd. It is speculated that because of the very low surface coverage of the neopentane reaction intermediates, only surface atoms that form the strongest bonds are catalytically active—that is, Pt—rather than all surface atoms. The dominant role of Pd, therefore, appears to be to (slightly) modify Pt rather than to contribute to the catalytic conversion.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".