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Record W4390503571 · doi:10.1038/s41377-023-01349-2

Single-emitter super-resolved imaging of radiative decay rate enhancement in dielectric gap nanoantennas

2024· article· en· W4390503571 on OpenAlexaff
R. Margoth Córdova‐Castro, Bart van Dam, Alberto Lauri, Stefan A. Maier, Riccardo Sapienza, Yannick De Wilde, Ignacio Izeddin, Valentina Krachmalnicoff

Bibliographic record

VenueLight Science & Applications · 2024
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsUniversity of Ottawa
FundersEngineering and Physical Sciences Research CouncilAgence Nationale de la RechercheResearch Councils UK
KeywordsDielectricRadiative transferPurcell effectCommon emitterPlasmonRefractive indexOpticsMaterials scienceOptoelectronicsNanostructureSpontaneous emissionMolecular physicsPhysicsNanotechnologyLaser

Abstract

fetched live from OpenAlex

High refractive index dielectric nanoantennas strongly modify the decay rate via the Purcell effect through the design of radiative channels. Due to their dielectric nature, the field is mainly confined inside the nanostructure and in the gap, which is hard to probe with scanning probe techniques. Here we use single-molecule fluorescence lifetime imaging microscopy (smFLIM) to map the decay rate enhancement in dielectric GaP nanoantenna dimers with a median localization precision of 14 nm. We measure, in the gap of the nanoantenna, decay rates that are almost 30 times larger than on a glass substrate. By comparing experimental results with numerical simulations we show that this large enhancement is essentially radiative, contrary to the case of plasmonic nanoantennas, and therefore has great potential for applications such as quantum optics and biosensing.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.264
Teacher spread0.249 · 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 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

Citations20
Published2024
Admission routes1
Has abstractyes

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