Reliable element-specific d-band analysis of transition metal nanoparticles using X-ray absorption spectroscopy
Bibliographic record
Abstract
Metallic nanoparticles (NPs) have been extensively studied as improved catalysts due to their unique atomic structures and electronic properties. A reliable strategy to characterize the electronic properties, specifically the d-band structure, of metal NPs is challenging. In this work, we present a general strategy for fitting the X-ray absorption near edge spectroscopy (XANES) spectrum to accurately determine the electronic properties in metal NPs, and deduce d-band parameters such as the d-band width and d-band center position. In conjunction with valence band X-ray photoelectron spectroscopy (VBXPS), our fitting analysis reliably calibrates the XANES spectrum such that effects of instrumental and core-hole broadening are minimized, and the d-band structure can be accurately determined. For a series of palladium (Pd) NPs, we use our XANES fitting analysis approach to characterize the Pd d-band, and identify trends in the electronic properties consistent with prior literature reports. In closing, we propose a mechanism in which the Pd d-band changes due to size, and surface effects based on our element-specific XANES fitting analysis within the framework of the popular Nørskov d-band model of transition metal surfaces. We anticipate that this experimental d-band analysis methodology will be useful for studying structure-property relationships and catalysis on monometallic and multimetallic NPs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".