Interplay of strain and indium incorporation in InGaN/GaN dot-in-a-wire nanostructures by scanning transmission electron microscopy
Bibliographic record
Abstract
The interplay between strain and composition is at the basis of heterostructure design to engineer new properties. The influence of the strain distribution on the incorporation of indium during the formation of multiple InGaN/GaN quantum dots (QDs) in nanowire (NW) heterostructures has been investigated, using the combined techniques of geometric phase analysis of atomic-resolution images and quantitative elemental mapping from core-loss electron energy-loss spectroscopy within scanning transmission electron microscopy. The variation in In-content between successive QDs within individual NWs shows a dependence on the magnitude of compressive strain along the growth direction within the underlying GaN barrier layer, which affects the incorporation of In-atoms to minimize the local effective strain energy. Observations suggest that the interfacial misfit between InGaN/GaN within the embedded QDs is mitigated by strain partitioning into both materials, and results in normal stresses inflicted by the presence of the surrounding GaN shell. These experimental measurements are linked to the local piezoelectric polarization fields for individual QDs, and are discussed in terms of the photoluminescence from an ensemble of NWs.
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) | 0.000 | 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".