Arrayed and entangled silicon nanowires using Au nanoparticle catalysts prepared by pulsed laser-induced dewetting
Bibliographic record
Abstract
The use of pulsed laser-induced dewetting (PLiD) is reported as a novel approach in the fabrication of Au nanoparticle (NP) catalytic arrays for the growth of Si nanowires (NWs) by chemical vapor deposition using SiCl 4 in the presence of H 2 . On polished Si substrates, PLiD generates Au NP catalysts with long-range order and narrow size distributions. It has been shown that the monodispersed distribution of Au NPs provides consistent diameter control of the as-grown Si NWs. A systematic exploration of the Si NW synthesis time, temperature, and gas flow rates illustrates a level of tunability in terms of morphology, be it arrayed or entangled Si NWs, with varying experimental parameters. An investigation of the effect of growth temperature also showed that Si NWs can be synthesized at temperatures as low as 700 °C when using SiCl 4 as the precursor. The use of porous Si substrates enabled direct observation of the diameter-dependent growth due to the simultaneous presence of three Au NP size distributions. Growth from the small- and medium-sized Au NP catalysts occurred first, followed by that from the large-sized Au NPs, which was only observed at extended times or high SiCl 4 flow rates. The delayed onset of growth from the large-sized Au NPs is due to the longer time to achieve Si super-saturation of larger catalyst NPs. The morphology and diameter control of the as-grown Si NWs reported in this work makes this approach potentially useful toward applications such as nanoelectronics, sensors, and lithium ion battery electrodes depending on the desired morphology.
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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".