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Record W1986697969 · doi:10.1002/adfm.200500379

Efficient Infrared Electroluminescent Devices Using Solution‐Processed Colloidal Quantum Dots

2005· article· en· W1986697969 on OpenAlex
Gerasimos Konstantatos, Chun-Yuan Huang, Larissa Levina, Zheng‐Hong Lu, Edward H. Sargent

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

VenueAdvanced Functional Materials · 2005
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsElectroluminescenceMaterials scienceQuantum efficiencyOptoelectronicsNanocrystalNanocompositeRadiative transferQuantum dotPolymerInfraredComposite numberNanotechnologyOpticsComposite materialPhysics

Abstract

fetched live from OpenAlex

Abstract We report efficient electroluminescence in the near‐infrared from PbS–MEH‐PPV (poly(2‐methoxy‐5‐(2′‐ethylhexyloxy)‐1,4‐phenylenevinylene)) large‐area, solution‐cast nanocomposite devices. We employ multivariate optimization of the structural and materials components that govern the radiative, energy‐transfer, and bipolar‐injection efficiencies into the devices. As a result, we report an external electroluminescence quantum efficiency of 0.27 %, which corresponds to an internal electroluminescence quantum efficiency of 1.9 %. The very best devices exhibit internal‐radiative‐efficiency‐limited performance and not transport‐ or capture‐limited performance, indicating that further gains in efficiency may be achieved if the internal radiative efficiency of the nanocrystal–polymer composite can be further increased without compromising transfer and device bipolar‐injection efficiency.

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)0.001
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 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 score0.010
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.023
GPT teacher head0.244
Teacher spread0.221 · 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