Asymmetries in Transition Metal XPS Spectra: Metal Nanoparticle Structure, and Interaction with the Graphene-Structured Substrate Surface
Bibliographic record
Abstract
Transition-metal XPS spectra have traditionally been considered to possess a natural asymmetry, extending to the high-binding-energy side. This is based on the fact that these spectra have generally been found experimentally to have such an asymmetry, as well as on the confirmation of asymmetry offered by the Doniach-Sunjić equation, an equation based on the proposal that the conduction electron scattering amplitude for interband absorption or emission in metals, at the Fermi level, is a singularity. Our discovery that metal nanoparticles, prepared under vacuum and characterized without exposure to air, have symmetric peaks, which become asymmetric with time, informed us that these peak asymmetries have other sources. On the basis of our belief that all metal spectra are composed of symmetric peaks, where the asymmetries are attributed to overlapping minor peaks that are consistent with known physical and chemical phenomena associated with that metal, we have shown that, for the metals that we have studied, these asymmetries contain much information, otherwise unavailable, on the structures, contaminants, oxidation, and interfacial interactions of nanoparticle surfaces. The existence of this information has been demonstrated for several metals, and its value is shown by its use in explaining the strong interfacial bonding of the nanoparticles with substrates having graphene structures. A possible future research direction is offered in the field of metal-metal interactions in nanoparticle alloys.
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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 | 0.001 |
| 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".