Tunable emission from InAs quantum dots on InP nanotemplates
Bibliographic record
Abstract
Selective area chemical beam epitaxy is used to fabricate submicron [100]-oriented InP ridges with well-defined, defect-free (011) sidefacets and (001) tops. Following the deposition of two monolayers of InAs on such nanotemplates and subsequent capping with InP, photoluminescence spectra show for wider ridges strong emission from a thin InAs quantum well and, as the ridge width is reduced, a gradual appearance of a quantum dot emission at lower energy. The method allows continuous tuning on a given sample in a single growth run of both the quantum dot density and the emission wavelength. The result is a consequence of adatom diffusion from the ridge sidefacets onto the top (001) facet, which increases the amount of InAs beyond the critical thickness for three-dimensional nucleation to occur. Compared with growth on planar InP(001) substrates, InAs self-assembled quantum dots grown on these nanotemplates are more uniform as revealed by a twofold reduction in emission linewidth at 4 K.
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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".