Unusual ferromagnetism in CoSi nanowires from internal and interfacial defects
Bibliographic record
Abstract
The diamagnetic semimetal CoSi presents unanticipated ferromagnetism as CoSi/SiO2 nanowires (NWs). Using first-principles calculations, we offer physical insights into the origins of this unusual magnetism. Due to the distorted and dangling bonds near the NW surface with different bond lengths, the transition metal (Co) d-orbital electron spin up and spin down populations become asymmetric from the exchange interactions, providing the mechanism for some of the measured magnetization. However, the distorted and dangling bonds are clearly not the only factor contributing to the magnetization of the NWs. The transmission electron microscopy selected area electron diffraction analysis of the CoSi region suggested a superlattice structure existed in the cubic CoSi, and defects existing as ordered vacancies in the CoSi were present. The simulation's results for the Co moment in the CoSi NWs without these ordered vacancies, but incorporating the surface and internal spin moments, is only 0.1638 μ(B)/atom Co, which is a ∼80% shortfall compared to the experimental value of 0.8400 μ(B)/atom Co. When the effects of ordered vacancies are incorporated into the simulation, 0.7886 μ(B) per surface Co atom, a much better match with the experimental value (within ∼6%), indicating that the internal ordered vacancies in the CoSi NWs are the dominant mechanism of ferromagnetism.
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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".