Atomic Ordering in InGaN Alloys within Nanowire Heterostructures
Bibliographic record
Abstract
Ternary III-nitride based nanowires (NWs) are promising for optoelectronic applications by offering advantageous design and control over composition, structure, and strain. Atomic-level chemical ordering in wurtzite InGaN alloys along the c-plane direction with a 1:1 periodicity within InGaN/GaN NW heterostructures was investigated by scanning transmission electron microscopy. Atomic-number-sensitive imaging contrast was used to simultaneously assign the In-rich and Ga-rich planes and determine the crystal polarity to differentiate unique sublattice sites. The nonrandom occupation of the c-planes in the InGaN alloys is confirmed by the occurrence of additional superlattice spots in the diffraction pattern within the ternary alloy. Compositional modulations in the ordered InGaN was further studied using atomic-resolution elemental mapping, outlining the substantial In-enrichment. Confirming the preferential site occupation of In-atoms provides experimental validation for the previous theoretical model of ordered InGaN alloys in bulk epilayers based on differences in surface site energy. Therefore, this study strongly suggests that atomic ordering in InGaN has a surface energetics-induced origin. Optimization of atomic ordering, in particular in III-nitride NW heterostructures, could be an alternative design tool toward desirable structural and compositional properties for various device applications operating at longer visible wavelengths.
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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) | 0.000 | 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".