Tunability of room-temperature ferromagnetism in spintronic semiconductors through nonmagnetic atoms
Bibliographic record
Abstract
The implementation and control of room-temperature ferromagnetism (RTFM) by adding magnetic atoms to a semiconductor's lattice has been one of the most important problems in solid-state physics in the last decade. Herein we report on the mechanism that allows RTFM to be tuned by the inclusion of nonmagnetic aluminum in nickel ferrite. This material, ${\mathrm{NiFe}}_{2\ensuremath{-}x}{\mathrm{Al}}_{x}{\mathrm{O}}_{4}$ ($x=0,0.5,1.5$), has already shown much promise for magnetic semiconductor technologies, and we are able to add to its versatility technological viability with our results. The site occupancies and valencies of Fe atoms (${\mathrm{Fe}}^{3+}\phantom{\rule{4pt}{0ex}}{\mathrm{T}}_{d}, {\mathrm{Fe}}^{2+}\phantom{\rule{4pt}{0ex}}{\mathrm{O}}_{h}$, and ${\mathrm{Fe}}^{3+}\phantom{\rule{4pt}{0ex}}{\mathrm{O}}_{h}$) can be methodically controlled by including aluminum. Using the fact that aluminum strongly prefers a 3+ octahedral environment, we can selectively fill iron sites with aluminum atoms, and hence specifically tune the magnetic contributions for each of the iron sites, and therefore the bulk material as well. Interestingly, the influence of the aluminum is weak on the electronic structure, allowing one to retain the desirable electronic properties while achieving desirable magnetic properties.
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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) | 0.002 | 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".