Introducing and manipulating magnetic dopant exchange interactions in semiconductor nanowires
Bibliographic record
Abstract
The ability to control both spin and charge degrees of freedom in semiconductor nanostructrures is at heart of spintronic and quantum information technologies. Magnetically-doped semiconductor nanowires have emerged as a promising platform for spintronics, which warrants the exploration of their synthesis, electronic structure, and magnetic properties. Here we demonstrate the preparation of manganese-doped GaN and SnO2 nanowires by chemical vapor deposition and solvothermal methods, respectively. The investigation of both systems by electron microscopy and x-ray absorption spectroscopy at ensemble and single nanowire levels indicates that manganese dopants exist in a dual oxidation state, Mn2+ and Mn3+, with Mn2+ being the majority species. X-ray magnetic circular dichroism studies of individual nanowires suggest ferromagnetic interactions of manganese dopants, and the nanowire orientation-dependent magnetization owing to the magnetocrystalline anisotropy. The results of these studies demonstrate quantitative determination of the dopant electronic structure at the molecular level, and allow for a prediction of the magnetic properties of diluted magnetic semiconductor nanowires based on their orientation and geometry.
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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".