Spin polarization and<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>g</mml:mi></mml:math>-factor enhancement in graphene nanoribbons in a magnetic field
Bibliographic record
Abstract
We provide a systematic quantitative description of spin polarization in armchair and zigzag graphene nanoribbons (GNRs) in a perpendicular magnetic field. We first address spinless electrons within the Hartree approximation, studying the evolution of the magnetoband structure and formation of the compressible strips. We discuss the potential profile and the density distribution near the edges and the difference and similarities between armchair and zigzag edges. Accounting for the Zeeman interaction and describing the spin effects via the Hubbard term, we study the spin-resolved subband structure and relate the spin polarization of the system at hand to the formation of the compressible strips for the case of spinless electrons. At high magnetic field the calculated effective $g$ factor varies around a value of $\ensuremath{\langle}{g}^{*}\ensuremath{\rangle}\ensuremath{\approx}2.25$ for armchair GNRs and $\ensuremath{\langle}{g}^{*}\ensuremath{\rangle}\ensuremath{\approx}3$ for zigzag GNRs. An important finding is that in zigzag GNRs the zero-energy mode remains pinned to the Fermi energy and becomes fully spin polarized for all magnetic fields, which, in turn, leads to a strong spin polarization of the electron density near the zigzag edge. Because of this the effective $g$ factor in zigzag GNRs is strongly enhanced at low fields reaching values up to ${g}^{*}\ensuremath{\approx}30$. This is in contrast to armchair GNRs, where the effective $g$ factor at low field is close to its bare value, $g=2$.
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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".