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Influence of Impurity Spin Dynamics on Quantum Transport in Epitaxial Graphene

2015· article· en· W1860443953 on OpenAlexafffund
Samuel Lara‐Avila, Sergey Kubatkin, Oleksiy Kashuba, Joshua Folk, Silvia Lüscher, Rositza Yakimova, T. J. B. M. Janssen, Alexander Tzalenchuk, Vladimir I. Fal’ko

Bibliographic record

VenuePhysical Review Letters · 2015
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversity of British Columbia
FundersEuropean Research CouncilVetenskapsrådetStiftelsen för Strategisk ForskningKnut och Alice Wallenbergs StiftelseDeutsche ForschungsgemeinschaftCanada Foundation for InnovationNatural Sciences and Engineering Research Council of CanadaStiftelsen för Strategisk ForskningEuropean CommissionGraphene Flagship
KeywordsCondensed matter physicsGrapheneQuantum decoherenceWeak localizationSpin (aerodynamics)Magnetic fieldRelaxation (psychology)PhysicsQuantumMaterials scienceQuantum mechanicsMagnetoresistance

Abstract

fetched live from OpenAlex

Experimental evidence from both spin-valve and quantum transport measurements points towards unexpectedly fast spin relaxation in graphene. We report magnetotransport studies of epitaxial graphene on SiC in a vector magnetic field showing that spin relaxation, detected using weak-localization analysis, is suppressed by an in-plane magnetic field B(∥), and thereby proving that it is caused at least in part by spinful scatterers. A nonmonotonic dependence of the effective decoherence rate on B(∥) reveals the intricate role of the scatterers' spin dynamics in forming the interference correction to the conductivity, an effect that has gone unnoticed in earlier weak localization studies.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.331
Teacher spread0.305 · 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 source (direct Gemma or distilled Codex), 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

Citations16
Published2015
Admission routes2
Has abstractyes

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