Chemical Insight into the Origin of Red and Blue Photoluminescence Arising from Freestanding Silicon Nanocrystals
Bibliographic record
Abstract
Silicon nanocrystals (Si NCs) are attractive functional materials. They are compatible with standard electronics and communications platforms and are biocompatible. Numerous methods have been developed to realize size-controlled Si NC synthesis. While these procedures produce Si NCs that appear identical, their optical responses can differ dramatically. Si NCs prepared using high-temperature methods routinely exhibit photoluminescence agreeing with the effective mass approximation (EMA), while those prepared via solution methods exhibit blue emission that is somewhat independent of particle size. Despite many proposals, a definitive explanation for this difference has been elusive for no less than a decade. This apparent dichotomy brings into question our understanding of Si NC properties and potentially limits the scope of their application. The present contribution takes a substantial step forward toward identifying the origin of the blue emission that is not expected based upon EMA predictions. It describes a detailed comparison of Si NCs obtained from three of the most widely cited procedures as well as the conversion of red-emitting Si NCs to blue emitters upon exposure to nitrogen-containing reagents. Analysis of the evidence is consistent with the hypothesis that the presence of trace nitrogen and oxygen even at the parts per million level in Si NCs gives rise to the blue emission.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".