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Record W2242763174 · doi:10.1021/acsami.5b08097

Very High Brightness Quantum Dot Light-Emitting Devices via Enhanced Energy Transfer from a Phosphorescent Sensitizer

2015· article· en· W2242763174 on OpenAlexafffund
Hossein Zamani Siboni, Bahareh Sadeghimakki, Siva Sivoththaman, Hany Aziz

Bibliographic record

VenueACS Applied Materials & Interfaces · 2015
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhosphorescenceMaterials scienceQuantum dotFörster resonance energy transferAcceptorOptoelectronicsBrightnessQuantum efficiencyAnnealing (glass)Phosphorescent organic light-emitting diodePhotochemistryFluorescenceOpticsChemistryPhysics

Abstract

fetched live from OpenAlex

We demonstrate very efficient and bright quantum dot light-emitting devices (QDLEDs) with the use of a phosphorescent sensitizer and a thermal annealing step. Utilizing CdSe/CdS core/shell quantum dots with 560 nm emission peak, bis(4,6-difluorophenylpyridinatoN,C2) picolinatoiridium as a sensitizer, and thermal annealing at 50 °C for 30 min, green-emitting QDLEDs with a maximum current efficiency of 23.9 cd/A, a power efficiency of 31 lm/W, and a brightness of 65,000 cd/m(2) are demonstrated. The high efficiency and brightness are attributed to annealing-induced enhancements in both the Forster resonance energy transfer (FRET) process from the phosphorescent energy donor to the QD acceptor and hole transport across the device. The FRET enhancement is attributed to annealing-induced diffusion of the phosphorescent material molecules from the sensitizer layer into the QD layer, which results in a shorter donor-acceptor distance. We also find, quite interestingly, that FRET to a QD acceptor is strongly influenced by the QD size, and is generally less efficient to QDs with larger sizes despite their narrower bandgaps.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.017
GPT teacher head0.214
Teacher spread0.197 · 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; both teacher heads agree on what is shown here.

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

Citations33
Published2015
Admission routes2
Has abstractyes

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