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Record W2783504182 · doi:10.1039/c7nr07683e

The role of polyethylenimine in enhancing the efficiency of quantum dot light-emitting devices

2018· article· en· W2783504182 on OpenAlexafffund
Tyler Davidson‐Hall, Hany Aziz

Bibliographic record

VenueNanoscale · 2018
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsRegional Municipality of WaterlooUniversity of Waterloo
FundersWaterloo Institute for Nanotechnology, University of WaterlooNatural Sciences and Engineering Research Council of Canada
KeywordsPolyethylenimineQuantum dotOptoelectronicsMaterials scienceCharge (physics)NanotechnologyElectronBalance (ability)ChemistryPhysicsNuclear physicsQuantum mechanics

Abstract

fetched live from OpenAlex

Although the use of polyethylenimine (PEI) in quantum dot light-emitting devices (QDLEDs) has recently been found to improve efficiency, the mechanism behind this increase has been disputed in the literature. In this work, we conduct investigations to elucidate the role of PEI in enhancing QDLED efficiency. Spectroscopic studies of devices with a phosphorescent marking layer reveal that the PEI layer increases, rather than decreases, the generation of excitons within the hole transporting layer indicative of increased electron injection. Delayed electroluminescence measurements corroborate these findings as devices with a PEI interlayer exhibit a greater concentration of excess mobile and trapped electrons. We attribute the improvement in efficiency despite the ensuing increased charge imbalance within the devices to the passivation of exciton quenching at the ZnO/QD interface. The increase in efficiency predominantly occurs over low driving currents which is particularly attractive for the brightness targets of display applications. Furthermore, despite the increased charge imbalance, the PEI passivation layer appears to have little effect on QDLED stability. This shows that excess electrons and Auger quenching by unneutralized electrons are not detrimental to QDLED stability.

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)0.002
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 score0.007
Threshold uncertainty score0.199

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
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 head0.011
GPT teacher head0.236
Teacher spread0.225 · 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

Citations38
Published2018
Admission routes2
Has abstractyes

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