(Invited) Silicon Quantum Dots: From Single-Dot Studies to Highly Luminescent Ensembles
Bibliographic record
Abstract
The luminescent properties of nanoscale silicon were demonstrated in 1990 triggering the interest in Si nanostructures for optical emission. While the luminescence was quite strong even at room temperature, the micro-second long PL lifetimes revealed an inherited indirect bandgap from bulk Si. The luminescence mechanism was heavily debated whether quantum confinement really played a role or the emission would be due entirely from surface states. Many studies have confirmed the quantum confinement model but it has also been shown that the emission can be widely tuned by changing the surface passivation or by adding different ligands to the shell. The physics of the emission was however obscured by inhomogeneous line broadening due to the large size dispersion of the emitting entities in porous silicon or in matrices containing silicon nanocrystals. This called for single-dot spectroscopy studies as demonstrated for single molecules and later for nanocrystals of direct bandgap semiconductors. In this talk I will start by reviewing our work on PL emission and spectroscopy of individual Si quantum dots. These were initially fabricated using electron-beam lithography, plasma etching and oxidation resulting in micron spaced Si quantum dots that could be individually distinguished in an optical microscope. More recently we have also looked at more “randomly” spaced Si nanocrystals as well as colloidal nanocrystals dispersed on a wafer. These single-dot studies have revealed many of the phenomena observed for the II-VI nanocrystals such as on/off intermittency (blinking), spectral diffusion and narrow linewidth. Indeed, the linewidth at low temperatures was found to be ~250 µeV, limited by system resolution while the room temperature homogenous linewidth was shown to depend on the surface passivation and ligand shell. Recently, we have also performed absorption measurements of single nanocrystals revealing several distinct absorption levels above the fundamental bandgap that can be compared to theoretical calculations. Finally, in a collaboration with the group of Prof Veinot at the Univ. of Alberta, we have examined colloidal dispersions of Si nanocrystals fabricated from HSQ (hydrogen silsesquioxane). Recent data show very high quantum yields approaching 70 % for these nanocrystals. Lifetime measurements suggest that near 100 % internal quantum efficiency can be reached paving the way for many applications.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".