The influence of conjugated alkynyl(aryl) surface groups on the optical properties of silicon nanocrystals: photoluminescence through in-gap states
Bibliographic record
Abstract
Developing new methods, other than size and shape, for controlling the optoelectronic properties of semiconductor nanocrystals is a highly desired target. Here we demonstrate that the photoluminescence (PL) of silicon nanocrystals (SiNCs) can be tuned in the range 685-800 nm solely via surface functionalization with alkynyl(aryl) (phenylacetylene, 2-ethynylnaphthalene, 2-ethynyl-5-hexylthiophene) surface groups. Scanning tunneling microscopy/spectroscopy on single nanocrystals revealed the formation of new in-gap states adjacent to the conduction band edge of the functionalized SiNCs. PL red-shifts were attributed to emission through these in-gap states, which reduce the effective band gap for the electron-hole recombination process. The observed in-gap states can be associated with new interface states formed via (-Si-C≡C-) bonds in combination with conjugated molecules as indicated by ab initio calculations. In contrast to alkynyl(aryl)s, the formation of in-gap states and shifts in PL maximum of the SiNCs were not observed with aryl (phenyl, naphthalene, 2-hexylthiophene) and alkynyl (1-dodecyne) surface groups. These outcomes show that surface functionalization with alkynyl(aryl) molecules is a valuable tool to control the electronic structure and optical properties of SiNCs via tuneable interface states, which may enhance the performance of SiNCs in semiconductor devices.
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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".