Formation of Hybrid Silicon Nanostructures via Capillary Instability Triggered in Inductively‐Coupled‐Plasma Torch Synthesized Ultra‐Thin Silicon Nanowires
Bibliographic record
Abstract
This paper provides a report on the formation of two main classes of hybrid silicon nanostructures via the capillary instability induced in ultra‐thin silicon nanowires (SiNWs) when subject to high temperature annealing. The first class of hybrid Si nanostructures shows a high‐level nanostructural order, and regroups (i) periodic strings of almond‐shaped Si nanocrystals (SiNCs) having average dimension of 3 nm and connected by ultra‐thin (≈2 nm) SiNWs and (ii) spherical SiNC chains (mean diameter of 6 nm, spaced by 16 nm on average), both embedded into silica NWs. In the second class of hybrid Si nanostructures, the SiNCs have different dimensions (in the 3–14 nm size range) and shapes, or a modulated Si core inside a SiO2 shell, thus exhibiting much higher nanostructural complexity. The self‐assembly of such nanostructures is related to the gas ambient under which the thermal treatment is performed. However, by increasing the annealing temperature, the SiNWs’ cores preferentially evolve toward the spherical SiNC chain morphology. The ultra‐thin diameter (2–3 nm) of the initial SiNWs is a key feature to induce the hybrid Si nanostructures formation. This study opens up the prospects of in situ controlling the formation of Si nanocrystals inside silica NWs, which will lead to the tailoring of their optoelectronic properties.
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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) | 0.001 | 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 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".