Platinum Nanoparticle Formation by Pulsed Laser-induced Dewetting and Its Application as Catalyst in Silicon Nanowire Growth
Bibliographic record
Abstract
Pulsed laser-induced dewetting (PLiD) of Pt thin films of various thicknesses is explored to form Pt nanoparticles (NPs) on SiO 2 /Si substrates for a wide range of laser fluences and irradiation times. The threshold fluence with a single laser pulse to achieve full dewetting into isolated Pt NPs from the 3.5 nm-thick Pt films is found to be 322–400 mJ cm –2 . Spherical low-index-faceted Pt NPs are obtained above this threshold fluence for all Pt thin films studied in this work. Based on the X-ray diffraction analysis, pure and crystalline Pt NPs are produced without Pt silicide contamination originating from the Pt–Si interdiffusion. The difference in the thermal conductivity between the thin SiO 2 surface layer and the underlying bulk Si substrates as well as the dependence of the time to reach thermal equilibrium and the effective temperature of the Pt NPs on the substrate thermal conductivity cause an initial increase in NP size at the very short irradiation time from 0.1 to 0.5 s, followed by a relatively constant size as the time increases to 20 s. Additionally, the nanoisland formation in the Volmer–Weber growth of Pt thin films on SiO 2 /Si substrates and the mechanism of nucleation and growth of holes governing the PLiD process lead to an independent relationship between the Pt NP size and the initial film thickness. Application of the PLiD-produced Pt NPs in metal-catalyzed chemical vapor deposition growth of Si nanowires (SiNWs) yields crystalline SiNWs with an axial growth rate of 6.4 μm min –1, following the vapor–liquid–solid axial growth mechanism accompanied by radial vapor–solid growth. The influence of the Pt NP shape and facets on the SiNW growth is also examined, showing that the spherical low-index-faceted NPs result in slower NW growth as compared to the nonspherical high-index-faceted ones of similar size.
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".