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Record W2995794850 · doi:10.1002/adfm.201907003

Polymer Light‐Emitting Electrochemical Cells with Bipolar Electrode‐Dynamic Doping and Wireless Electroluminescence

2019· article· en· W2995794850 on OpenAlexafffund
Shiyu Hu, Jun Gao

Bibliographic record

VenueAdvanced Functional Materials · 2019
Typearticle
Languageen
FieldEngineering
TopicOrganic Light-Emitting Diodes Research
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceOptoelectronicsElectrochemical cellElectrodeElectroluminescenceElectrochemistryDopingAnodeVoltageNanotechnologyLayer (electronics)Electrical engineeringChemistry

Abstract

fetched live from OpenAlex

Abstract This progress report reviews the background and recent developments of polymer light‐emitting electrochemical cells (PLECs). The PLECs of interest have a planar configuration and contain bipolar electrodes (BPEs). A BPE is an electrically floating conductor immersed in an electrochemical cell that contains redox species. When the electrochemical cell is polarized with an externally applied voltage bias, coupled redox reactions are induced wirelessly at the extremities of the BPEs due to the development of a sufficient interfacial potential difference. BPEs can have a dramatic effect on the doping pattern and the emission profiles of PLECs. In a bulk homojunction PLEC containing a large number of dispersed micro‐BPEs, the turn‐on response and light output are greatly enhanced when multiple light‐emitting p–n junctions form throughout the active layer. PLECs offer a solid‐state platform on which the bipolar electrochemistry phenomena can be investigated, and the understanding of the complex PLEC processes can be improved. This progress report highlights several new BPE types as well as their potential applications for device performance enhancement and for the visualized screening of functional materials.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score1.000

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.002
GPT teacher head0.176
Teacher spread0.174 · 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.

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

Citations16
Published2019
Admission routes2
Has abstractyes

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