Strain analysis of highly scalable single InAs/InP quantum dots in a stress-sensitive environment
Bibliographic record
Abstract
We perform an experimental and computational study of the effects of external stress and intermixing on single site-selected InAs/InP quantum dots in a highly scalable stress-sensitive environment. While such effects are well known for their ability to tune emission spectra, little is known on how they influence emission shell spacing, electron-hole effective mass renormalization, and the physical size of the embedded quantum dot, which are all important parameters affecting the intended functionality. We show excellent agreement between experiment and finite-element solutions of the coupled Navier and Schrödinger equations, including recent atomistic pseudopotential calculations in the literature. These results indicate that using single self-assembled quantum dots in highly scalable, stress-sensitive settings as active elements in future bottom-up nanosystems offers greater versatility to not only quantum information systems where they serve as scalable single-photon sources but also to ultra-sensing capabilities in future nano-electro-mechanical architectures.
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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".