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Record W2417369468 · doi:10.1021/acsphotonics.5b00757

Nanoparticle Acoustic Resonance Enhanced Nearly Degenerate Four-Wave Mixing

2016· article· en· W2417369468 on OpenAlexafffund
Dao Xiang, Reuven Gordon

Bibliographic record

VenueACS Photonics · 2016
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFour-wave mixingMaterials scienceMixing (physics)Resonance (particle physics)Rayleigh scatteringDegenerate energy levelsRefractive indexSemiconductorNanoparticleTerahertz radiationOpticsOptoelectronicsRange (aeronautics)Band gapNonlinear opticsMolecular physicsPhysicsAtomic physicsNanotechnologyLaser

Abstract

fetched live from OpenAlex

Nanoparticles have acoustic resonances in the 10 GHz to THz range, which makes them interesting for resonantly enhanced four-wave mixing applications such as frequency conversion. Using a nearly degenerate four-wave mixing setup and comparing with Rayleigh–Gans–Debye scattering, we estimate a nonlinear refractive index, n 2, of approximately 2 × 10 –5 cm 2 /MW for 22 nm polystyrene nanospheres in water at a difference frequency of 43 GHz. This is nearly 3 orders of magnitude larger than the typical below-band-gap nonlinear response of semiconductors. This strong high-frequency response may be ideal for four-wave frequency conversion in all-optical information technologies, with appropriately chosen nanoparticle sizes.

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 score0.065
Threshold uncertainty score0.636

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 head0.209
Teacher spread0.191 · 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

Citations9
Published2016
Admission routes2
Has abstractyes

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