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Record W2022179484 · doi:10.1063/1.1894590

Plasmonically enhanced diffusive and subdiffusive metal nanoparticle-dye random laser

2005· article· en· W2022179484 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueApplied Physics Letters · 2005
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRandom lasers and scattering media
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRandom laserMaterials scienceLaser linewidthNanoparticleScatteringLaserSurface plasmon resonanceFluenceLight scatteringPlasmonAbsorption (acoustics)DielectricSilver nanoparticleOptoelectronicsOpticsNanotechnologyWavelengthComposite material

Abstract

fetched live from OpenAlex

We report on surface plasmon (SP)-enhanced random laser emission from a suspension of silver nanoparticles in a laser dye operating at diffusive and subdiffusive scattering strengths. SP resonance enhances the scattering cross section, while the geometrical cross section remains small, thus providing a large gain volume. The localized electromagnetic field near the particle surface leads to enhanced absorption of excitation light and larger amplification of fluorescence. The metal-nanoparticle-based random laser yields larger linewidth narrowing at lower pump fluence threshold than a dielectric-scatterer-based random laser under equivalent conditions. These findings open the door to studies of applications related to light amplification assisted by SP in metallic nanoparticles.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.017
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)0.0000.000

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 head0.005
GPT teacher head0.198
Teacher spread0.193 · 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