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Record W4394805416 · doi:10.1021/acs.nanolett.4c00776

Harmonic Generation up to Fifth Order from Al/Au/CuS Nanoparticle Films

2024· article· en· W4394805416 on OpenAlexaff
Yueming Yan, Nathan J. Spear, Qingzhou Meng, Mahi R. Singh, Janet E. Macdonald, Richard F. Haglund

Bibliographic record

VenueNano Letters · 2024
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsWestern University
Fundersnot available
KeywordsPlasmonSecond-harmonic generationHarmonicsChalcogenideNanoparticleLocalized surface plasmonMaterials scienceHarmonicNonlinear opticsDipoleHigh harmonic generationOptoelectronicsSurface second harmonic generationHeterojunctionSurface plasmon resonanceSurface plasmonOpticsNanotechnologyPhysicsNonlinear systemQuantum mechanics

Abstract

fetched live from OpenAlex

Dual heterostructures integrating noble-metal and copper chalcogenide nanoparticles have attracted a great deal of attention in nonlinear optics, because coupling of their localized surface plasmon resonances (LSPRs) substantially enhances light-matter interactions through local-field effects. Previously, enhanced cascaded third-harmonic generation was demonstrated in Au/CuS heterostructures mediated by harmonically coupled surface plasmon resonances. This suggests a promising approach for extending nonlinear enhancement to higher harmonics by adding an additional nanoparticulate material with higher-frequency harmonic resonances to the hybrid films. Here we report the first observation of enhanced cascaded fourth- and fifth-harmonic generation in Al/Au/CuS driven by coupled LSPRs at the fundamental (1050 nm), second harmonic (525 nm), and third harmonic (350 nm) of the pump frequency. An analytical model based on incoherent dipole-dipole interactions among plasmonic nanoparticles accounts for the observed enhancements. The results suggest a novel design for efficiently generating higher harmonics in resonant plasmonic structures by means of multiple sum-frequency cascades.

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 score0.337
Threshold uncertainty score0.999

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)0.0000.002

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 head0.021
GPT teacher head0.248
Teacher spread0.226 · 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

Citations10
Published2024
Admission routes1
Has abstractyes

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