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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 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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.017
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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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