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Record W3100098989 · doi:10.1002/adfm.202007119

Efficient Solution‐Processed Hyperfluorescent OLEDs with Spectrally Narrow Emission at 840 nm

2020· article· en· W3100098989 on OpenAlexafffund
Afshin Shahalizad, Alexandre Malinge, Lei Hu, Grégory Laflamme, Louis Haeberlé, David Moffat Myers, Jian Mao, W. G. Skene, Stéphane Kéna‐Cohen

Bibliographic record

VenueAdvanced Functional Materials · 2020
Typearticle
Languageen
FieldEngineering
TopicOrganic Light-Emitting Diodes Research
Canadian institutionsUniversité de MontréalPolytechnique Montréal
FundersCanada Foundation for InnovationSamsung Advanced Institute of Technology
KeywordsOLEDMaterials scienceElectroluminescenceOptoelectronicsFluorescenceQuantum efficiencyPhosphorescenceDopantExcitonDopingOpticsNanotechnology

Abstract

fetched live from OpenAlex

Abstract To reduce cost and improve environmental sustainability, there continues to be an important need for the development of efficient organic light‐emitting diodes (OLEDs) that do not rely on heavy metal‐containing compounds. In particular, the efficiency of fluorescent near‐infrared (NIR) OLEDs continues to lag well‐behind that of their platinum‐containing counterparts. Low efficiencies in this spectral range mainly arise from the low quantum yields of fluorescent NIR emitters due to the energy gap law and inefficient harvesting of triplet excitons. In this paper, a thermally activated delayed fluorescent (TADF) material is used as the assistant dopant to demonstrate pure NIR‐emitting fluorescent OLEDs with an external quantum efficiency of up to 3.8%, with an electroluminescence maximum at 840 nm and a spectral full‐width at half‐maximum of < 40 nm. The efficiency is more than three times higher than that of the best previously reported fluorescent OLEDs in this spectral range and approaches that achievable with the best platinum‐containing phosphorescent emitters.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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)0.0020.001

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 head0.011
GPT teacher head0.198
Teacher spread0.186 · 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 source (direct Gemma or distilled Codex), 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

Citations63
Published2020
Admission routes2
Has abstractyes

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