(Invited) Photoluminescence Efficiency of Germanium Dots Self-Assembled on Oxides
Bibliographic record
Abstract
Self-assembled Ge quantum dots were formed by in-situ thermal annealing of a thin amorphous Ge layer deposited by molecular beam epitaxy either on a thin porous TiO 2 layer grown on SiO 2 on Si(001) or directly on the SiO 2 layer itself. For samples with dot diameters ranging from 10 to 35 nm, the dot photoluminescence (PL) appeared primarily as a wide near-infrared band peaked near 800 meV. The peak energy of the PL band reflects the average dot size and its shape depends on the dot size distribution. Using tight binding and effective mass theoretical models, we have analyzed the PL spectrum in terms of the dot size distribution. The observed size distribution determined from transmission electron and atomic force microscopy allowed the determination of the nonlinear increase in the PL efficiency with decreasing dot diameter. Although the absolute intensities of the PL from the samples vary, the calculated efficiency curves are all well fitted by straight lines on a log-log plot, with essentially the same slope for all samples, thereby demonstrating that under the weak confinement regime investigated here there is a universal power-law increase in PL efficiency with decreasing dot size. Knowing this generic PL efficiency, we show that it is possible to evaluate the size distribution of Ge dots from their PL energy dependence.
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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".