Platinum-Catalyzed Silicon Nanowire Growth─Evidence for a Switch from Vapor–Liquid–Solid to Vapor–Solid–Solid Mechanism with Platinum Nanoparticle Size
Bibliographic record
Abstract
The Si nanowire (SiNW) growth via chemical vapor deposition was explored using single crystalline Pt nanoparticles (NPs) of different sizes ranging from 17 ± 6 to 72 ± 22 nm produced by thermal dewetting to study the impact of Pt NP catalyst size on the SiNW growth kinetics and structural properties. Through the analysis of NW growth rate, incubation time, and catalyst tip morphology post growth, it has been shown that the use of the 17 ± 6 nm Pt NP catalysts facilitates the vapor–liquid–solid (VLS) SiNW growth with faster growth rate and longer incubation time, whereas the use of larger Pt NP catalysts with sizes of 27 ± 7 and 72 ± 22 nm allows the vapor–solid–solid (VSS) growth, showing slower growth rate with shorter incubation time. Therefore, it is possible to change the growth mechanism of SiNWs by varying the Pt NP size. According to the X-ray diffraction (XRD) and selected-area electron diffraction analyses, all the produced SiNWs were single crystalline having a diamond cubic crystal structure with the lattice constant of 5.428–5.431 Å without change in the crystallographic orientation along the nanowires. The catalysts remained at the tip of the SiNWs via both VLS and VSS growth, and the phase was confirmed to be PtSi using XRD and energy-dispersive X-ray analyses. This suggests that both VLS and VSS growth using the Pt NPs produced in situ PtSi as the catalyst in operation. All SiNWs showed growth along the ⟨111⟩ direction. The SiNWs via VLS growth using the smaller size Pt NPs (17 ± 6 nm) had less defects than the SiNWs via VSS growth using the Pt NP catalysts of larger sizes of 27 ± 7 and 72 ± 22 nm, which had kinks, twinning, and dislocations. The understanding obtained from this work on the effect of Pt NP size on the growth mechanism and structural defects provides a facile way to control the production of SiNWs for appropriate applications such as lithium-ion batteries and electronics.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".