Formation of light-emitting silicon nanoclusters in SiO<sub>2</sub>
Bibliographic record
Abstract
Silicon nanoclusters/nanocrystals (Si-nc) in an SiO2 matrix exhibit strong visible luminescence, and so are of interest in the pursuit of a silicon-based light emitter for optoelectronics. We have investigated the formation of Si-nc by implanting excess Si at 90 keV into SiO2 films and then annealing to form nanoclusters by precipitation and ripening. The use of ion implantation provides control over composition and so allows us to optimize the light output. Positron annihilation provides information on vacancy-type defects produced during implantation. Our results suggest that defects may play a key role in Si-nc formation. The depth and size distributions of Si-nc are obtained by transmission electron microscopy, and are correlated with light emission measured by photoluminescence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".