Tuning Manganese Dopant Spin Interactions in Single GaN Nanowires at Room Temperature
Bibliographic record
Abstract
Control of electron spins in individual magnetically doped semiconductor nanostructures has considerable potential for quantum information processing and storage. The manipulations of dilute magnetic interactions have largely been restricted to low temperatures, limiting their potential technological applications. Among the systems predicted to be ferromagnetic above room temperature, Mn-doped GaN has attracted particular attention, due to its attractive optical and electrical properties. However, the experimental data have been inconsistent, and the origin of the magnetic interactions remains unclear. Furthermore, there has been no demonstration of tuning the dopant exchange interactions within a single nanostructure, which is necessary for the design of nanoscale spin-electronic (spintronic) devices. Here we directly show for the first time intrinsic magnetization of manganese dopants in individual gallium nitride nanowires (NWs) at room temperature. Using high-resolution circularly polarized X-ray microscopy imaging, we demonstrate the dependence of the manganese exchange interactions on the NW orientation with respect to the external magnetic field. The crystalline anisotropy allows for the control of dilute magnetization in a single NW and the application of bottom-up approaches, such as in situ nanowire growth control or targeted positioning of individual NWs, for the design of networks for quantum information technologies.
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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".