Hidden defects in silicon nanowires
Bibliographic record
Abstract
Recent publications have reported the presence of hexagonal phases in Si nanowires. Most of these reports were based on 'odd' diffraction patterns and HRTEM images—'odd' means that these images and diffraction patterns could not be obtained on perfect silicon crystals in the classical diamond cubic structure. We analyze the origin of these 'odd' patterns and images by studying the case of various Si nanowires grown using either Ni or Au as catalysts in combination with P or Al doping. Two models could explain the experimental results: (i) the presence of a hexagonal phase or (ii) the presence of defects that we call 'hidden' defects because they cannot be directly observed in most images. We show that in many cases one direction of observation is not sufficient to distinguish between the two models. Several directions of observations have to be used. Secondly, conventional TEM images, i.e. bright-field two-beam and dark-field images, are of great value in the identification of 'hidden' defects. In addition, slices of nanowires perpendicular to the growth axis can be very useful. In the studied nanowires no hexagonal phase with long range order is found and the 'odd' images and diffraction patterns are mostly due to planar defects causing superposition of different crystal grains. Finally, we show that in Raman experiments the defect-rich NWs can give rise to a Raman peak shifted to 504–511 cm⁻¹ with respect to the Si bulk peak at 520 cm⁻¹, indicating that Raman cannot be used to identify a hexagonal phase.
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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".