Interfacial Electronic Structure of Gold Nanoparticles on Si(100): Alloying versus Quantum Size Effects
Bibliographic record
Abstract
Gold nanoparticles (Au NPs) were prepared on a native-oxide-covered Si(100) substrate by sputter-deposition followed by thermal annealing. The size of Au NPs could be controlled in the range of 8-48 nm by varying the sputter-deposition time and post-annealing temperature. The interparticle separation was found to be directly related to the size of Au NPs, with smaller separations for particles of smaller size. The surface morphology, crystal structure, and interfacial composition of the chemical states of these supported Au NPs were studied as a function of their average size by using scanning electron microscopy, glancing-incidence X-ray diffraction, and depth-profiling X-ray photoelectron spectroscopy (XPS), respectively. The new Au 4f7/2 peak found at 1.1-1.2 eV higher in binding energy than that for the metallic Au feature (at 84.0 eV) can be attributed to the formation of Au silicide at the interface between Au NPs and the Si substrate. Depth-profiling XPS experiments revealed no discernible change in the binding energies of the Au silicide and metallic Au 4f features with increasing Ar+ sputtering time, indicating that the Au-to-silicide interface is abrupt. Furthermore, the shift in the Au 5d5/2 valence band to a higher binding energy and the reduction of the Au 5d spin-orbit splitting with increasing Ar+ sputtering time also support the formation of Au silicide. No clear evidence for the quantum size effect was observed for the supported NPs. The finite density of state at the Fermi level and the fixed Au 4f7/2 peak position clearly indicate the metallic nature of the Au silicide at the Au-Si interface.
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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".