Interfacial Electronic Structure of Gold Nanoparticles on Si(100): Alloying versus Quantum Size Effects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it