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Record W2117301459 · doi:10.1103/physrevb.90.235420

Renormalization of nanoparticle polarizability in the vicinity of a graphene-covered interface

2014· article· en· W2117301459 on OpenAlexfundno aff
Jaime E. Santos, M. I. Vasilevskiy, N. M. R. Peres, Georgi Smirnov, Yu. V. Bludov

Bibliographic record

VenuePhysical Review B · 2014
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsnot available
FundersEuropean Social FundFundação para a Ciência e a TecnologiaEuropean CommissionMountain Equipment Co-operative
KeywordsPolarizabilityGrapheneCondensed matter physicsDipolePlasmonMaterials scienceDielectricSurface plasmonTerahertz radiationAbsorption (acoustics)PhysicsOpticsNanotechnologyOptoelectronicsQuantum mechanics

Abstract

fetched live from OpenAlex

We study the electromagnetic properties of a metamaterial consisting of polarizable (nano)particles and a single graphene sheet placed at the interface between two dielectrics. We show that the particle's polarizability is renormalized because of the electromagnetic coupling to surface plasmons supported by graphene, which results in a dispersive behavior, different for the polarizability components corresponding to the induced dipole moment, parallel and perpendicular to the graphene sheet. In particular, this effect is predicted to take place for a metallic particle whose bare polarizability in the terahertz (THz) region is practically equal to the cube of its radius (times $4\ensuremath{\pi}{\ensuremath{\varepsilon}}_{0}$). This opens the possibility to excite surface plasmons in graphene and enhance its absorption in the THz range by simply using a monolayer of metallic particles randomly deposited on top of it, as we show by explicit calculations.

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)0.000
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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.015
GPT teacher head0.290
Teacher spread0.275 · 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

Citations14
Published2014
Admission routes1
Has abstractyes

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