Effect of thermal treatment on the growth, structure and luminescence of nitride-passivated silicon nanoclusters
Bibliographic record
Abstract
Silicon nanoclusters (Si-ncs) embedded in silicon nitride films have been studied to determine the effects that deposition and processing parameters have on their growth, luminescent properties, and electronic structure. Luminescence was observed from Si-ncs formed in silicon-rich silicon nitride films with a broad range of compositions and grown using three different types of chemical vapour deposition systems. Photoluminescence (PL) experiments revealed broad, tunable emissions with peaks ranging from the near-infrared across the full visible spectrum. The emission energy was highly dependent on the film composition and changed only slightly with annealing temperature and time, which primarily affected the emission intensity. The PL spectra from films annealed for duration of times ranging from 2 s to 2 h at 600 and 800°C indicated a fast initial formation and growth of nanoclusters in the first few seconds of annealing followed by a slow, but steady growth as annealing time was further increased. X-ray absorption near edge structure at the Si K- and L3,2-edges exhibited composition-dependent phase separation and structural re-ordering of the Si-ncs and silicon nitride host matrix under different post-deposition annealing conditions and generally supported the trends observed in the PL spectra.
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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) | 0.000 | 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".