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Record W2319049082 · doi:10.1021/jp412223m

Plasmon-Enhanced Triplet–Triplet Annihilation Using Silver Nanoplates

2014· article· en· W2319049082 on OpenAlexafffund
Kianoosh Poorkazem, Amelia V. Hesketh, Timothy L. Kelly

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2014
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsUniversity of Saskatchewan
KeywordsPhoton upconversionQuantum yieldMaterials sciencePhotochemistryPlasmonOptoelectronicsSurface plasmon resonanceQuantum dotAnnihilationNanoparticleNanotechnologyDopingChemistryOpticsFluorescencePhysics

Abstract

fetched live from OpenAlex

Photon upconversion processes have attracted substantial interest as a means of circumventing the Shockley–Queisser limit for single-junction photovoltaic devices. Despite this promise, the quantum yield of most upconversion processes is very low at the light intensities typical of solar radiation (∼100 mW/cm 2 ). Additionally, bimolecular upconversion processes that rely on molecular diffusion (e.g., triplet–triplet annihilation) typically see further reductions in quantum yield when the upconverting chromophores are confined to a solid state or thin film matrix. Here we report a plasmon-based enhancement of the triplet–triplet annihilation process when silver nanoplates are embedded in poly(methyl methacrylate) thin films containing the upconverting materials palladium(II) octaethylporphyrin and 9,10-diphenylanthracene. The silver nanoplates are synthesized with localized surface plasmon resonance bands tailored to overlap strongly with the Q-band of the porphyrin, leading to enhanced light absorption within the film and higher overall triplet concentrations. Optimization of the silver nanoplate loading leads to a nearly 10-fold increase in the upconverted light intensity compared with control samples containing no silver.

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)0.001
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 score0.009
Threshold uncertainty score0.296

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.239
Teacher spread0.222 · 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

Citations49
Published2014
Admission routes2
Has abstractyes

Explore more

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