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Record W4210319941 · doi:10.1088/1361-6528/ac4fe2

Inhomogeneous linear responses and transport in armchair graphene nanoribbons in the presence of elastic scattering

2022· article· en· W4210319941 on OpenAlexaff
Mousa Bahrami, Panagiotis Vasilopoulos

Bibliographic record

VenueNanotechnology · 2022
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsConcordia UniversityBitume Québec
Fundersnot available
KeywordsWave vectorCondensed matter physicsScatteringLong wavelength limitWavelengthMaterials sciencePolarization (electrochemistry)GrapheneElectronic band structureGraphene nanoribbonsConductivityRelaxation (psychology)Kubo formulaPhysicsQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract Within linear-response theory we derive a response function that thoroughly accounts for the influence of elastic scattering and is valid beyond the long-wavelength limit. We use the theory to evaluate the polarization function and the conductivity in metallic armchair graphene nanoribbons in the Lindhard approximation for intra-band and inter-band transitions and for a relaxation time τ that is not constant. We obtain a logarithmic behaviour in the scattering-independent polarization function not only for intra-band transitions, as is usually the case for one-dimensional systems, but also for inter-band transitions. Modifying the screening wave vector and the impurity density in the long-wavelength limit strongly influences the relaxation time. In contrast, for large wave vectors, this modification leads to a conservative value of τ . We show that the imaginary part of the impurity-dependent conductivity varies with the wave vector while its scattering-independent part exists only for a single value of the wave vector.

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)0.000
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 score0.271

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.017
GPT teacher head0.267
Teacher spread0.250 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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