Crystallized silicon nanostructures — experimental characterization and atomistic simulations
Bibliographic record
Abstract
We have synthesized silicon nanocrystalline structures from thermal annealing of thin film amorphous silicon-based multilayers. The annealing procedure that was carried out in vacuum at temperatures up to 1100 °C is integrated in a X-ray diffraction (XRD) setup for real-time monitoring of the formation phases of the nanostructures. The microstructure of the crystallized films is investigated through experimental measurements combined with atomistic simulations of realistic nanocrystalline silicon (nc-Si) models. The multilayers consisting of uniformly alternating thicknesses of hydrogenated amorphous silicon and silicon oxide (SiO2) were deposited by plasma enhanced chemical vapor deposition on crystalline silicon and Corning glass substrates. The crystallized structure consisting of nc-Si structures embedded in an amorphous matrix were further characterized through XRD, Raman spectroscopy, and Fourier transform infrared measurements. We are able to show the different stages of nanostructure formation and how the sizes and the crystallized mass fraction can be controlled in our experimental synthesis. The crystallized silicon structures with large crystalline filling fractions exceeding 50% have been simulated with a robust classical molecular dynamics technique. The crystalline filling fractions and structural order of nc-Si obtained from this simulation are compared with our Raman and XRD measurements.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".