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Record W2107288017 · doi:10.1039/c5nr01525a

Transient dynamics of magnetic Co–graphene systems

2015· article· en· W2107288017 on OpenAlexaff
Bin Wang, Jianwei Li, Fuming Xu, Yadong Wei, Jian Wang, Hong Guo

Bibliographic record

VenueNanoscale · 2015
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsMcGill University
FundersNational Natural Science Foundation of ChinaNatural Science Foundation of Shenzhen CityNational Science Foundation
KeywordsGrapheneTransient (computer programming)Materials scienceTraverseSpin (aerodynamics)Dynamics (music)ElectronNanotechnologyMolecular dynamicsCondensed matter physicsChemical physicsPhysicsComputer scienceQuantum mechanicsAcousticsThermodynamics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.238

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.279
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2015
Admission routes1
Has abstractyes

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