Atom probe composition and <i>in situ</i> electronic structure of epitaxial quantum dot ensembles
Bibliographic record
Abstract
Dense arrays of semiconductor quantum dots are currently employed in highly efficient quantum dot lasers for data communications and other applications. Traditionally, the electronic properties of such quantum nanostructures have been treated as isolated objects, with the degree of hybridization between neighboring quantum dots and the wetting layer left unexplored. Here, we use atom probe tomography and transmission electron microscopy to uncover the three-dimensional (3D) composition profile of a high-density ensemble of epitaxial InAs/GaAs quantum dots. The subnanometer compositional data are used to construct the 3D local band edge and simulate the electronic eigenstates within the dense quantum dot ensemble using the finite element method. This in situ electronic simulation reveals a high degree of hybridization between neighboring quantum dots and the wetting layer, in stark contrast to the usual picture of isolated quantum nanostructures. The simulated transition energies are compared with low-temperature photoluminescence. This work has important applications for quantum dot laser design and paves the way to engineering ensemble effects in quantum dot lasers and other quantum nanostructures.
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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".