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Record W2132013047 · doi:10.1039/c5cp02249e

Surface plasmon enhanced up-conversion from NaYF<sub>4</sub>:Yb/Er/Gd nano-rods

2015· article· en· W2132013047 on OpenAlexafffund
Peng‐Hui Wang, Zhiqiang Li, Walter J. Salcedo, Zhuo Sun, Sumei Huang, Alexandre G. Brolo

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2015
Typearticle
Languageen
FieldMaterials Science
TopicOptical Coatings and Gratings
Canadian institutionsUniversity of Victoria
FundersBeijing Municipal Science and Technology CommissionNational Natural Science Foundation of ChinaShanghai Science and Technology Development FoundationUniversity of VictoriaScience and Technology Commission of Shanghai MunicipalityNatural Science Foundation of Shanghai
KeywordsMaterials scienceSurface plasmonExcited stateSurface plasmon resonancePlasmonYttriumAnalytical Chemistry (journal)NanoparticleOpticsOptoelectronicsAtomic physicsChemistryNanotechnology

Abstract

fetched live from OpenAlex

The surface plasmons that are enabled by grating coupling in two-dimensional gold nano-particle arrays (AuNPAs) affected the spectral characteristics of the up-conversion (UC) emission from Yb(3+)-Er(3+)-Gd(3+) co-doped sodium yttrium fluoride (NaYF4:Yb/Er/Gd) nano-rods. The red emission of NaYF4:Yb/Er/Gd nano-rods at 660 nm (excited with a 980 nm diode laser) was significantly enhanced by the interaction with the AuNPAs. The geometric characteristics of the gold nanoparticles influenced the position of the surface plasmon resonance, and their near field strengths. The intensity of the red emission normalized versus the green emission reached 1.4, measured against a reference film in the absence of the metallic nanostructures. The lifetime for the green and red emission decreased steadily as the periodicity decreased (relative to the reference), reaching about 6% reduction for the 350 nm AuNPA. A qualitative agreement was obtained between the experimental results and finite difference time domain (FDTD) calculations.

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 categoriesMeta-epidemiology (narrow), Insufficient 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 score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.016
GPT teacher head0.241
Teacher spread0.224 · 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

Citations15
Published2015
Admission routes2
Has abstractyes

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