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Record W2399501404 · doi:10.1021/acs.nanolett.6b00770

Threshold for Terahertz Resonance of Nanoparticles in Water

2016· article· en· W2399501404 on OpenAlexafffund
Dao Xiang, Jörg Rottler, Reuven Gordon

Bibliographic record

VenueNano Letters · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsUniversity of British ColumbiaUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrostrictionTerahertz radiationResonance (particle physics)NanoparticleMaterials scienceNonlinear systemContinuous waveDegenerate energy levelsLaserParticle (ecology)VibrationMolecular physicsAcoustic waveRange (aeronautics)OpticsPhysicsOptoelectronicsNanotechnologyAtomic physicsAcousticsPiezoelectricityQuantum mechanics

Abstract

fetched live from OpenAlex

Nanoparticle vibrations are coupled to light through electrostriction, which gives nonlinear optical scattering. We investigated the acoustic response of 2 nm gold nanoparticles using a nearly degenerate four-wave mixing experimental configuration and show that the nonlinear response suddenly turns on at low powers (<100 mW) for continuous-wave (CW) lasers. The observed nonlinear response is a million times larger than typical electronic nonlinearities. The threshold implies a dramatic change in the quality factor of the vibrating nanoparticles, 4 orders of magnitude larger than usual hydrodynamic theory predicts. It is as if the water is removed altogether, which we speculate is the result of the vibrating particle pushing away the water molecules to form a stable cavity. Because these acoustic vibrations extend to terahertz frequencies, there is potential to harness this effect for high speed optical data processing, as well as to probe the dynamics of proteins all having acoustic modes in this range.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.011
GPT teacher head0.241
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), 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

Citations21
Published2016
Admission routes2
Has abstractyes

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