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Record W4220812023 · doi:10.1002/adfm.202200350

Dual‐Phase Regulation for High‐Efficiency Perovskite Light‐Emitting Diodes

2022· article· en· W4220812023 on OpenAlexaff
Kebin Lin, Chuanzhong Yan, Randy P. Sabatini, Wenjing Feng, Jianxun Lu, Kaikai Liu, Dongxin Ma, Yueyue Shen, Yaping Zhao, Mingliang Li, Chengbo Tian, Liqiang Xie, Edward H. Sargent, Zhanhua Wei

Bibliographic record

VenueAdvanced Functional Materials · 2022
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Toronto
FundersHuaqiao UniversityNatural Science Foundation of Fujian ProvinceNational Natural Science Foundation of China
KeywordsLight-emitting diodeOptoelectronicsMaterials sciencePerovskite (structure)PassivationDiodeCommon emitterLattice (music)ExcitonQuantum efficiencyRealization (probability)Layer (electronics)NanotechnologyChemistryPhysicsCondensed matter physics

Abstract

fetched live from OpenAlex

Abstract Perovskite light‐emitting diodes (Pero‐LEDs) have attracted significant attention due to their high color purity and solution processing, presenting potential applications for next‐generation solid‐state lighting and displays. Continued materials development has shown that passivating non‐radiative defects can improve device performance. In theory, CsPbBr 3 &Cs 4 PbBr 6 should be a model emitter for Pero‐LEDs, as its lattice matching provides ideal passivation and efficient exciton confinement. However, the low charge transport of Cs 4 PbBr 6 has so far hindered device performance. Herein, a dual‐phase regulation method to grow a CsPbBr 3 &Cs 4 PbBr 6 perovskite layer that enables efficient electrical injection is developed. This leads to the realization that Pero‐LEDs with a maximum EQE of 22.3% with a luminance of 10,050 cd m −2 , and the devices show a T 50 of 59 h at 130 cd m −2 .

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 categoriesInsufficient 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 score0.057
Threshold uncertainty score0.999

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.229
Teacher spread0.219 · 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

Citations62
Published2022
Admission routes1
Has abstractyes

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