InAs single quantum dots in Wurtzite InP nanowires emitting at telecommunication wavelengths
Bibliographic record
Abstract
Quantum dots (QDs) embedded in semiconductors nanowires are of great interest for photonic quantum technologies. We have previously reported that QDs in nanowires could be an efficient source for single photons [1] and entangled photon pairs [2] that is required for applications in quantum computation, quantum cryptography and quantum optics. For these later applications, the source has to meet requirements such as high symmetry, high brightness, high extraction efficiency, high-fidelity entanglement and a precise position control at the nanoscale level. The selective area vapor-liquid-solid (VLS) growth process is a very suitable technique to synthesize nanowires with high yield and homogeneity [3]. Moreover, the optical and electronic properties of these QD based sources are controllable by manipulating the nanowire dimension, dot size, and composition. We have been so far very successful in producing high optical quality single InAsP quantum dots in InP nanowire that are emitting in the range of 890-970 nm. The emission wavelength is controlled by the arsenic percentage in the InAsP dot which is about 25%.
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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".