Formation of Hybrid Silicon Nanostructures via Capillary Instability Triggered in Inductively‐Coupled‐Plasma Torch Synthesized Ultra‐Thin Silicon Nanowires (Phys. Status Solidi B 7/2019)
Bibliographic record
Abstract
The formation of hybrid silicon nanostructures is studied in article number 1800620 by Marta Agati et al. Ultra-thin silicon nanowires (diameter 2–3 nm), synthesized via an inductively coupled plasma (ICP) torch process, were subjected to thermal treatments under different ambient gas. Formation of the nanostructures is ascribed to the capillary instability developed in the ultra-thin Si core as long as the temperature reaches values of 800–1200 °C. The resulting hybrid Si nanostructures consist of a string of Si nanocrystals (SiNCs) with different shapes and dimensions embedded in silica nanowires. The cover image shows different energy-filtered transmission electron microscopy (EFTEM) images, the high-resolution TEM image of an almond-shaped SiNC, and the capillary instability model. The EFTEM images, acquired on consecutive parts of these long (∼μm) hybrid Si nanostructures, illustrate the morphology of the Si core, which features a chapletlike Si nanostructure (on the left) and a chain of equallysized spherical SiNCs periodically displaced inside the silica nanowire (on the right). – This article belongs to a collection of 6 articles on “Nanostructures and Self-Assembly”, guestedited by Simona Boninelli, Isabelle Berbezier, Maurizio De Crescenzi, and David Grosso (cf. Preface, article no. 1900345).
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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".