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Record W4387304455 · doi:10.1002/andp.202300147

Magnetic Purcell Enhancement in a Nanoantenna‐Spherical Bragg Resonator Coupled System

2023· article· en· W4387304455 on OpenAlexafffund
Yalina García‐Puente, Raman Kashyap

Bibliographic record

VenueAnnalen der Physik · 2023
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMagnetic dipoleDielectricRadiative transferResonatorDipoleMagnetic fieldPurcell effectPlasmonResonance (particle physics)Materials sciencePhysicsOptoelectronicsOpticsSpontaneous emissionAtomic physicsLaser

Abstract

fetched live from OpenAlex

Abstract In this work, a novel approach for enhancing magnetic fields in all‐dielectric nanoantennas using Spherical Bragg Resonators (SBR) is proposed, which can boost quantum emitters' magnetic transitions. A matrix method has been used to optimize the magnetic dipole resonance of a SiO 2 /Si core‐shell spherical nanoantenna. The radiative and non‐radiative decay rate of a Eu 3+ emitter with a quantum efficiency of ∼80% is studied. The findings revealed that the magnetic dipole nanoantenna resonance coupling with the SBR mode significantly enhances the modal magnetic field. A 4‐layer SiO 2 /Si SBR results in a Purcell factor of , the highest it has found in the literature, to the best of the knowledge. The work offers a theoretical demonstration of the potential of SBR to improve the performance of dielectric nanoantennas.

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 categoriesInsufficient payload (model declined to judge)
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 score0.839
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.003

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 head0.234
Teacher spread0.219 · 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.

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

Citations6
Published2023
Admission routes2
Has abstractyes

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