General Control of Transition-Metal-Doped GaN Nanowire Growth: Toward Understanding the Mechanism of Dopant Incorporation
Bibliographic record
Abstract
We report the first synthesis and characterization of cobalt- and chromium-doped GaN nanowires (NWs), and compare them to manganese-doped GaN NWs. Samples were synthesized by chemical vapor deposition method, using cobalt(II) chloride and chromium(III) chloride as dopant precursors. For all three impurity dopants hexagonal, triangular, and rectangular NWs were observed. The fraction of NWs having a particular morphology depends on the initial concentration of the dopant precursors. While all three dopant ions have the identical effect on GaN NW growth and faceting, Co and Cr are incorporated at much lower concentrations than Mn. These findings suggest that the doping mechanism involves binding of the transition-metal intermediates to specific NW facets, inhibiting their growth and causing a change in the NW morphology. We discuss the doping concentrations of Mn, Co, and Cr in terms of differences in their crystal-field stabilization energies (DeltaCFSE) in their gas-phase intermediates and in substitutionally doped GaN NWs. Using iron(III) chloride and cobalt(II) acetate as dopant precursors we show that the doping concentration dependence on DeltaCFSE allows for the prediction of achievable doping concentrations for different dopant ions in GaN NWs, and for a rational choice of a suitable dopant-ion precursor. This work further demonstrates a general and rational control of GaN NW growth using transition-metal impurities.
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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".