New Insights into the ORR Catalysis on Pt Alloy Nanoparticles from an Element Specific <i>d</i>-Band Analysis
Bibliographic record
Abstract
Bolstered by their unique atomic structures and tailored compositions, nanoalloys exhibit extraordinary properties making them ideal materials to solve challenges in energy storage and conversion catalysis. However, a quantitative description of the structure–property relationships using an accurate descriptor-based model for nanoalloys, ranging from bimetallic to multimetallic compositions, is needed to drive efficient material design toward high-performance catalysis. In this work, we highlight the electronic property and catalytic activity relationship from an element specific d -band analysis of Pt-based alloy catalysts using X-ray absorption near-edge spectroscopy (XANES). Using a series of L1 0 -MPt/Pt (M = Fe, Co, Ni) core/shell alloy catalysts with well-defined atomic structures, we quantified subtle differences in the Pt d -electron states and correlated the Pt d -band structure to their superior catalytic activity toward the oxygen reduction reaction (ORR). Our analysis used the upper d -band edge position as a predictive descriptor for the mass activity toward the ORR instead of the commonly used d -band center position. Together with density functional theory calculations and Nørskov d -band theory, the upper d -band edge position for the Pt states, derived from experimental measurements, elucidates new physical insights into the ORR performance of the L1 0 -MPt/Pt core/shell catalysts. An element specific Pt d -band analysis using XANES overcomes challenges in traditional X-ray photoelectron spectroscopy-based valence d -band analysis, which cannot distinguish signals from independent elements in nanoalloys. Thus, the insights from the element specific d -band analysis presented in this work are a promising approach to determine structure–property relationships in a variety of transition metal nanoalloys and will be useful in the design of future high-performance catalysts.
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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".