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Record W2505739276 · doi:10.1103/physrevb.94.245404

Dephasing in strongly anisotropic black phosphorus

2016· article· en· W2505739276 on OpenAlexafffund
N. Hemsworth, V. Tayari, Francesca Telesio, Shao-Hua Xiang, Stefano Roddaro, Maria Caporali, Andrea Ienco, Manuel Serrano‐Ruiz, Maurizio Peruzzini, G. Gervais, Thomas Szkopek, Stefan Heun

Bibliographic record

VenuePhysical review. B./Physical review. B · 2016
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsMcGill University
FundersSeventh Framework ProgrammeEuropean Research CouncilCentre National de la Recherche ScientifiqueRussian Foundation for Basic ResearchFonds de recherche du Québec – Nature et technologiesNational Research FoundationNatural Sciences and Engineering Research Council of CanadaEuropean CommissionHydro-QuébecGraphene FlagshipScuola Normale Superiore
KeywordsDephasingAnisotropyCondensed matter physicsWeak localizationScatteringBlack phosphorusPhysicsElectronLattice (music)Magnetic fieldMagnetoresistanceOpticsQuantum mechanics

Abstract

fetched live from OpenAlex

Weak localization was observed in a black phosphorus field-effect transistor 65 nm thick. The weak localization behavior was found to be in excellent agreement with the Hikami-Larkin-Nagaoka model for fields up to 1 T, from which characteristic scattering lengths could be inferred. The temperature dependence of the phase coherence length ${L}_{\ensuremath{\varphi}}$ was investigated, and above 1 K, it was found to decrease weaker than the ${L}_{\ensuremath{\varphi}}\ensuremath{\propto}{T}^{\ensuremath{-}1/2}$ dependence characteristic of electron-electron scattering in the presence of elastic scattering in two dimensions. Rather, the observed power law was found to be close to that observed previously in quasi-one-dimensional systems such as metallic nanowires and carbon nanotubes.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.342
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.006

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 head0.019
GPT teacher head0.360
Teacher spread0.341 · 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.

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

Citations18
Published2016
Admission routes2
Has abstractyes

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