Growth optimization and optical properties of AlGaNAs alloys
Bibliographic record
Abstract
The effect of Al on the surface morphology of chemical beam epitaxy grown AlGaNAs alloys is studied. Pits attributed to N clustering appearing on the dilute nitride surface become smaller, denser, and more uniformly distributed in the presence of Al. This reveals that the introduction of Al results in more homogenous N atoms spatial distribution. A growth temperature study reveals the formation of 3D structures at high temperature due to phase separation. The density of these structures decreases, while their diameter and height increase when the temperature is raised from 380 °C to 565 °C. At growth temperatures in the 380–420 °C range, the phase separation is suppressed and the growth mode is 2D. At 420 °C, the N incorporation is also maximized, making it the optimum temperature. The absorption coefficient and the bandgap of AlGaNAs alloys are extracted from transmittance measurement. A good agreement is obtained between the experimentally measured bandgap and the theoretical values calculated using the band anticrossing model. A bandgap as low as 1.22 eV was reached using Al and N concentrations of ∼15% and ∼3.4%, respectively.
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 | 0.001 | 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".