Dopant Ion Concentration Dependence of Growth and Faceting of Manganese-Doped GaN Nanowires
Bibliographic record
Abstract
The effect of manganese dopant ions on the growth and faceting of manganese-doped GaN (Mn:GaN) nanowires is reported. Using the chemical vapor deposition method we synthesized high-quality internally doped GaN nanowires with varying concentrations of Mn precursor in the reaction mixtures. In all samples we observed nanowires having three distinct cross-section morphologies: hexagonal, triangular, and rectangular. These NWs were present in different ratios depending on the starting concentration of Mn ions. Using electron microscopies we found that the presence of Mn dopant ions inhibits nanowire growth and that the percentage of triangular and rectangular nanowires increases with increasing concentration of Mn precursor at the expense of hexagonal nanowires. We propose that Mn binding to specific nanowire facets plays a key role in governing Mn:GaN nanowire growth and dopant incorporation. These results allow for the preparation of Mn:GaN nanowires in a truly controlled fashion using the developed methodology and for the studies of Mn:GaN nanowire properties with respect to their structure and morphology.
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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".