Transient dynamics of magnetic Co–graphene systems
Bibliographic record
Abstract
We report the investigation of response time of spin resolved electron traversing through a magnetic Co-graphene nano-device. For this purpose, we calculate the transient current under a step-like upward pulse for this system from first principles using non-equilibrium Green's function (NEGF) formalism within the framework of density functional theory (DFT). In the absence of dephasing mechanisms, transient current shows a damped oscillatory behavior. The turn-on time of the magnetic Co-graphene nano-device was found to be around 5-20 femtoseconds, while the relaxation time can reach several picoseconds due to the damped oscillation of transient current for both majority spin and minority spin. The response time was determined by the resonant states below the Fermi level, but does not depend on the chirality of graphene and the amplitude of pulse bias. Each resonant state contributes to the damped oscillation of transient current with the same frequency and different decay rates. The frequency of the oscillation is half the pulse bias and the decay rate equals the lifetime of the corresponding resonant state. When inelastic phase-relaxing scattering is considered, the long duration oscillatory behavior of the transient current is suppressed and the relaxation time is reduced to hundreds of femtoseconds.
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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 | 0.001 |
| Open science | 0.001 | 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".