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Record W3038014797 · doi:10.1515/nanoph-2020-0213

Enhanced two‐photon photoluminescence assisted by multi‐resonant characteristics of a gold nanocylinder

2018· article· en· W3038014797 on OpenAlexaff
Artur Movsesyan, Gwénaëlle Lamri, Sergei Kostcheev, Anke Horneber, Annika Bräuer, Alfred J. Meixner, Monika Fleischer, Dai Zhang, Anne‐Laure Baudrion, Pierre‐Michel Adam

Bibliographic record

VenueNanophotonics · 2018
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
FundersBaden-Württemberg Stiftung
KeywordsPhotoluminescencePlasmonMaterials scienceOptoelectronicsNanorodQuantum dotDipoleNanoclustersExcitationPhotoluminescence excitationSpontaneous emissionMolecular physicsOpticsNanotechnologyPhysicsLaser

Abstract

fetched live from OpenAlex

Abstract Multi‐resonant plasmonic simple geometries like nanocylinders and nanorods are highly interesting for two‐photon photoluminescence and second harmonic generation applications, due to their easy fabrication and reproducibility in comparison with complex multi‐resonant systems like dimers or nanoclusters. We demonstrate experimentally that by using a simple gold nanocylinder we can achieve a double resonantly enhanced two‐photon photoluminescence of quantum dots, by matching the excitation wavelength of the quantum dots with a dipolar plasmon mode, while the emission is coupled with a radiative quadrupolar mode. We establish a method to separate experimentally the enhancement factor at the excitation and at the emission wavelengths for this double resonant system. The sensitivity of the spectral positions of the dipolar and quadrupolar plasmon resonances to the ellipticity of the nanocylinders and its impact on the two‐photon photoluminescence enhancement are discussed.

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 score0.010
Threshold uncertainty score0.877

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 head0.018
GPT teacher head0.266
Teacher spread0.247 · 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

Citations11
Published2018
Admission routes1
Has abstractyes

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