Giant magnetoresistance and spin Seebeck coefficient in zigzag α-graphyne nanoribbons
Bibliographic record
Abstract
We investigate the spin-dependent electric and thermoelectric properties of ferromagnetic zigzag α-graphyne nanoribbons (ZαGNRs) using density-functional theory combined with non-equilibrium Green's function method. A giant magnetoresistance is obtained in the pristine even-width ZαGNRs and can be as high as 10(6)%. However, for the doped systems, a large magnetoresistance behavior may appear in the odd-width ZαGNRs rather than the even-width ones. This suggests that the magnetoresistance can be manipulated in a wide range by the dopants on the edges of ZαGNRs. Another interesting phenomenon is that in the B- and N-doped even-width ZαGNRs the spin Seebeck coefficient is always larger than the charge Seebeck coefficient, and a pure-spin-current thermospin device can be achieved at specific temperatures.
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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".