Bismuth-surfactant-induced growth and structure of InAs/GaAs(110) quantum dots
Bibliographic record
Abstract
Abstract We explore the Bi-surfactant-directed self-assembly and structure of InAs quantum dots (QDs) grown on GaAs(110) by molecular beam epitaxy. The addition of a Bi flux during InAs deposition changes the InAs growth mode from two-dimensional (2D) Frank–van der Merwe to Stranski–Krastanov (SK), resulting in the formation of three-dimensional (3D) InAs islands on the surface. Furthermore, exposing static InAs 2D layers to Bi induces a rearrangement of the strained layer into 3D islands. We explore the effect of varying the InAs thickness and Bi flux for these two growth approaches, observing a critical thickness for 3D island formation in both cases. Characterization of (110) InAs QDs with high-resolution transmission electron microscopy reveals that larger islands grown by the SK mode are plastically relaxed, while small islands grown by the on-demand approach are coherent. Strain relaxation along the [ 1 1 ¯ 0 ] direction is achieved by 90° pure-edge dislocations with dislocation lines running along [001]. In contrast, strain relief along [001] is by 60° misfit dislocations. This behavior is consistent with observations of planar (In, Ga)As/GaAs(110) layers. These results illustrate how surfactant Bi can provoke and control QD formation where it normally does not occur.
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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".