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Record W2093195875 · doi:10.1364/oe.21.005643

Coherently-enabled environmental control of optics and energy transfer pathways of hybrid quantum dot-metallic nanoparticle systems

2013· article· en· W2093195875 on OpenAlexaff
Ali Hatef, Seyed M. Sadeghi, Simon Fortin-Deschênes, Étienne Boulais, Michel Meunier

Bibliographic record

VenueOptics Express · 2013
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsQuantum dotExcitonNanorodPlasmonQuantum dot laserMaterials scienceRefractive indexOptoelectronicsMolecular physicsPhysicsCondensed matter physicsNanotechnologySemiconductorSemiconductor laser theory

Abstract

fetched live from OpenAlex

It is well-known that optical properties of semiconductor quantum dots can be controlled using optical cavities or near fields of localized surface plasmon resonances (LSPRs) of metallic nanoparticles. In this paper we study the optics, energy transfer pathways, and exciton states of quantum dots when they are influenced by the near fields associated with plasmonic meta-resonances. Such resonances are formed via coherent coupling of excitons and LSPRs when the quantum dots are close to metallic nanorods and driven by a laser beam. Our results suggest an unprecedented sensitivity to the refractive index of the environment, causing significant spectral changes in the Förster resonance energy transfer from the quantum dots to the nanorods and in exciton transition energies. We demonstrate that when a quantum dot-metallic nanorod system is close to its plasmonic meta-resonance, we can adjust the refractive index to: (i) control the frequency range where the energy transfer from the quantum dot to the metallic nanorod is inhibited, (ii) manipulate the exciton transition energy shift of the quantum dot, and (iii) disengage the quantum dot from the metallic nanoparticle and laser field. Our results show that near meta-resonances the spectral forms of energy transfer and exciton energy shifts are strongly correlated to each other.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.565
Threshold uncertainty score0.704

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 head0.010
GPT teacher head0.182
Teacher spread0.171 · 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

Citations26
Published2013
Admission routes1
Has abstractyes

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