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Record W3023918823 · doi:10.1021/acs.jpclett.0c00757

Mechanisms of LiF Interlayer Enhancements of Perovskite Light-Emitting Diodes

2020· article· en· W3023918823 on OpenAlexafffund
Rafael Quintero‐Bermudez, Jeffrey Kirman, Dongxin Ma, Edward H. Sargent, Rafael Quintero‐Torres

Bibliographic record

VenueThe Journal of Physical Chemistry Letters · 2020
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Toronto
FundersDirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de MéxicoNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Research, Innovation and Science
KeywordsPerovskite (structure)OptoelectronicsDiodeMaterials scienceLight-emitting diodeChemistryCrystallography

Abstract

fetched live from OpenAlex

The use of LiF as a thin interlayer between the electron transport layer and cathode has played a pivotal role in remarkable advances in perovskite LEDs (PeLEDs); however, the mechanism behind the effect of LiF remains to be fully understood. Here, we report a combined experimental and computational study, from which we ascribe the benefits of a LiF interlayer to the migration of dissociated Li into the cathode and dissociated F into the anode. Electronic device simulations reveal that the former improves electron injection by lowering the Schottky barrier height, while the latter reduces the barrier width. These reduce turn-on voltage and improve current density and charge balance in LEDs. We fabricate PeLEDs with and without the LiF interlayer and link these materials and electronic phenomena to the device light-current-voltage characteristics. X-ray photoelectron spectroscopy obtained in sputter profiling of PeLEDs corroborates the dissociation of LiF.

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 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 score0.011
Threshold uncertainty score0.288

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 head0.008
GPT teacher head0.204
Teacher spread0.196 · 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

Citations22
Published2020
Admission routes2
Has abstractyes

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