InAs quantum wire induced composition modulation in an In0.53Ga0.37Al0.10As barrier layer grown on an InP substrate
Bibliographic record
Abstract
Composition modulations are observed by transmission electron microscopy in In0.53Ga0.37Al0.10As barrier layers that overgrow both single- and multilayer InAs quantum wire structures grown on an InP substrate. Indium-rich (gallium-deficient) regions were observed in the region of the barrier layer lying directly above individual quantum wires, while indium-deficient (gallium-rich) regions were detected in the barrier above the gaps between adjacent underlying quantum wires. The magnitude of such modulation was typically 7% (atomic percent) for both indium and gallium as estimated from the energy dispersive x-ray analysis. The origin of such composition modulations was determined by modeling the chemical potential distribution for indium and gallium on the growth front of the barrier layer at the initial capping stage of the quantum wires with finite element simulations. It is found that the number and positions of the indium-rich regions are determined by the combined effects of strain and surface energy distributions on the barrier material capping the quantum wires. Moreover the estimated magnitudes of the composition modulation for both indium and gallium from the finite element models are in good agreement with the experimental observations. This method provides a simple way to understand the origin of, and to estimate the magnitude of the quantum wire-induced composition modulation in the barrier layer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".