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Record W3210839621 · doi:10.1063/5.0071042

On the role of the energy loss function in the image force on a charge moving over supported graphene

2021· article· en· W3210839621 on OpenAlexafffund
María Rosa Preciado-Rivas, Milad Moshayedi, Z. L. Mišković

Bibliographic record

VenueJournal of Applied Physics · 2021
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGrapheneMethod of image chargesSubstrate (aquarium)PhononParticle (ecology)PhysicsMaterials scienceFunction (biology)Silicon carbidePlasmonSiliconCondensed matter physicsDielectricCharge (physics)OpticsNanotechnologyOptoelectronicsQuantum mechanicsComposite material

Abstract

fetched live from OpenAlex

We use a dielectric response theory to describe electrodynamic forces on a charged particle moving parallel to a supported two-dimensional layer. Using a Kramers–Kronig relation, we show that the image force on the particle can be expressed in terms of the energy loss function of the target materials. This enables us to analyze the stopping and the image forces on the particle on equal footing in the frequency–momentum domain encompassing all the energy loss channels in the target. Using the example of a graphene layer on a silicon carbide substrate, we show that both the image and stopping forces can be decomposed into contributions coming from two modes arising from hybridization of the sheet plasmon in doped graphene and a transverse optical phonon in the substrate.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.203
Teacher spread0.196 · 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 designTheoretical or conceptual
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

Citations6
Published2021
Admission routes2
Has abstractyes

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