Structural origin of the high-performance light-emitting InGaN/AlGaN quantum disks
Bibliographic record
Abstract
Ternary III-nitride-based nanowires with highly efficient light-emitting properties are essential for a broad range of applications. By using the selective area molecular-beam epitaxy method, InGaN/AlGaN quantum disks (QDs) embedded in hexagonal GaN nanowires were successfully grown. With the help of atomic-scale-resolved transmission electron microscopy and atom probe tomography, atomic ordering and other related structural information, such as crystallography and local chemistry, have been unambiguously revealed to provide unique insights into the exceptionally strong photoluminescence enhancements. A boomerang-shaped InGaN/AlGaN QD was identified, and atomic-level 1 : 1 periodic chemical ordering within the boomerang shaped AlGaN layers along the c-direction was revealed, confirming the preferential site occupation of Al-atoms. This type of growth provides a strong suppression of the quantum-confined Stark effect and is thus likely a very strong contributor to the exceptional properties. This work therefore enables us to directly establish the key structural elements necessary to understand the exceptionally strong emission exhibited by these materials. Optimization of the configurations of QDs could be an alternative design tool for developing various advanced LED device applications with well-designed structure and desirable optical properties.
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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) | 0.000 | 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".