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Record W2738567930 · doi:10.1021/acsphotonics.7b00344

Energy Losses and Transition Radiation in Multilayer Graphene Traversed by a Fast Charged Particle

2017· article· en· W2738567930 on OpenAlexafffund
Kamran Akbari, Z. L. Mišković, Silvina Seguí, J.L. Gervasoni, N. R. Arista

Bibliographic record

VenueACS Photonics · 2017
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversity of Waterloo
FundersAgencia Nacional de Promoción Científica y TecnológicaNatural Sciences and Engineering Research Council of CanadaConsejo Nacional de Investigaciones Científicas y Técnicas
KeywordsGraphenePlasmonTransition radiationElectronBilayer grapheneMaterials scienceCondensed matter physicsPhysicsMolecular physicsNanotechnologyOptoelectronicsQuantum mechanics

Abstract

fetched live from OpenAlex

We present a fully relativistic formulation of the energy loss of a charged particle traversing a number of graphene layers and apply it to the case of two spatially separated layers probed by an energetic electron. We focus on the THz frequency range, using a Drude model to describe the conductivity of graphene and allowing for different doping density in each layer. We distinguish two types of contributions to the electron energy loss: the energy deposited in graphene layers in the form of electronic excitations (Ohm losses), which include the excitation of Dirac plasmon polaritons (DPP), and the energy that is emitted in the form of transition radiation. We study in detail the contribution of each layer to the ohmic losses and analyze the directional decomposition of the radiation emitted in the half-spaces defined by the graphene planes. By increasing the interlayer distance and changing the relative doping densities in graphene layers, we find surprisingly strong asymmetries in both the directional and layer-wise decompositions with respect to the direction of motion of the external electron. A modal decomposition is also performed in the limit of vanishing damping in graphene, exposing quite intricate roles of bonding and antibonding hybridization between DPPs in ohmic losses.

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 score0.014
Threshold uncertainty score0.326

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 head0.015
GPT teacher head0.267
Teacher spread0.253 · 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

Citations25
Published2017
Admission routes2
Has abstractyes

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