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Record W3109122127 · doi:10.1088/1361-6528/abd05d

Optical quantum yield in plasmonic nanowaveguide

2020· article· en· W3109122127 on OpenAlexafffund
Mahi R. Singh, Grant Brassem, S. G. Yastrebov

Bibliographic record

VenueNanotechnology · 2020
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhotoluminescenceMaterials scienceQuantum yieldPlasmonQuantum dotMolecular physicsExcitonCondensed matter physicsOptoelectronicsPhysicsOptics

Abstract

fetched live from OpenAlex

We have developed a theory of the quantum yield for plasmonic nanowaveguide where the cladding layer is made of an ensemble of quantum dots and the core layer consists of an ensemble of metallic nanoparticles. The bound states of the confined probe photons in the plasmonic nanowaveguide are calculated using the transfer matrix method based on the Maxwell equations. It is shown that the number of bound states in the nanowaveguide depends on the dielectric properties of the core and cladding layers. The surface plasmon polaritons (SPPs) produced by the metallic nanoparticles interacts with the excitons of the quantum dots. The radiative and nonradiative linewidths of excitons in the quantum yield are calculated using the quantum mechanical perturbation theory. We have found that the quantum yield decreases as the dipole-dipole interaction between metallic nanoparticles increases. We have also calculated the photoluminescence and found that the enhancement in photoluminescence is due to the SPPs coupling. On the other hand, the quenching in the photoluminescence is due to the quantum yield. We compared our theory with experiments of a nanowaveguide where the core is fabricated from Ag- nanoparticles and the cladding is fabricated from the perovskite quantum dots. A good agreement between theory and experiments is found. Our analytical expressions of the quantum yield and photoluminescence can be used by experimentalists to proforma new types of experiments and for inventing new types of nanosensors and nanoswitches.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score1.000

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)0.0000.001

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 head0.032
GPT teacher head0.237
Teacher spread0.205 · 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 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
Published2020
Admission routes2
Has abstractyes

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