Formation of Au, Pt, and bimetallic Au–Pt nanostructures from thermal dewetting of single-layer or bilayer thin films
Bibliographic record
Abstract
Formation of Au, Pt, and bimetallic Au-Pt nanostructures by thermal dewetting of single-layer Au, Pt and bilayer Au-Pt thin films on Si substrates was systematically studied. The solid-state dewetting of both single-layer and bilayer metallic films was shown to go through heterogeneous void initiation followed by void growth via capillary agglomeration. For the single-layer of Au and Pt films, the void growth started at a temperature right above the Hüttig temperature, at which the atoms at the surface or at defects become mobile. Uniformly distributed Au (7 ± 1 nm to 33 ± 8 nm) and Pt (7 ± 1 nm) NPs with monodispersed size distributions were produced from complete dewetting achieved for thinner 1.7-5.5 nm thick Au and 1.4 nm thick Pt films, respectively. The NP size is strongly dependent on the initial thin film thickness, but less so on temperature and time. Thermal dewetting of Au-Pt bilayer films resulted in partial dewetting only, forming isolated nano-islands or large particles, regardless of sputtering order and total thin film thickness. The increased resistance to thermal dewetting shown in the Au-Pt bilayer films as compared to the individual Au or Pt layer is a reflection of the stabilizing effect that occurs upon adding Pt to Au in the bimetallic system. Energy dispersive x-ray spectroscopic analysis showed that the two metals in the bilayer films broke up together instead of dewetting individually. According to the x-ray diffraction analysis, the produced Au-Pt nanostructures are phase-segregated, consisting of an Au-rich phase and a Pt-rich phase.
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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) | 0.000 | 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".