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Record W2888136186 · doi:10.1088/1361-6463/aadc25

Collective and local energy transfer in biologically-hybridized systems of semiconductor quantum dots and metallic nanoantenna arrays

2018· article· en· W2888136186 on OpenAlexaff
Seyed M. Sadeghi, Rithvik R. Gutha, Christina Sharp, Ali Hatef

Bibliographic record

VenueJournal of Physics D Applied Physics · 2018
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsNipissing University
FundersNational Science Foundation
KeywordsQuantum dotEnergy transferSemiconductorNanotechnologyMaterials scienceMetalOptoelectronicsEnergy (signal processing)QuantumPhysicsEngineering physicsQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract We study polarization-dependent energy transfer and exciton dynamics in hybrid systems consisting of quantum dot bioconjugates and arrays of metallic nanoantennas. The size distribution of the quantum dots are used to investigate how excitons with different transition energies interact with the localized surface plasmon resonances (LSPRs) and collective surface lattice resonances (SLRs) supported by the arrays. We show that, depending on the projections of the electric dipoles of the quantum dots along the nanoantennas, they can interact with these resonances differently. This leads to polarization-dependent radiative and non-radiative decay of excitons, highlighting such systems can support two types of energy transfer mechanisms: (i) collective wavelength-dependent transfer of excitation energy from quantum dots to the arrays of the metallic nanoantennas via SLR (photonic-plasmonic path), and (ii) the local energy transfer to individual nanoantennas via localized surface plasmons (direct path). Our results also show that quantum dots with larger cores can have stronger interaction with SLRs. This suggests that such resonances can act as an energy drain that enhances cascaded energy transfer between quantum dots.

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 score0.076
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.022
GPT teacher head0.226
Teacher spread0.204 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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