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Record W4293193431 · doi:10.1039/d2ta01987f

A polymer/small-molecule binary-blend hole transport layer for enhancing charge balance in blue perovskite light emitting diodes

2022· article· en· W4293193431 on OpenAlexaff
Zhongkai Yu, Woo Hyeon Jeong, Keehoon Kang, Hochan Song, Xinyu Shen, Hyungju Ahn, Seok Woo Lee, Xiangyang Fan, Ji Won Jang, Su Ryong Ha, Jeong Wan Min, Jong Hyun Park, Jongmin Han, Eui Dae Jung, Myoung Hoon Song, Dong Wook Chang, Won Bin Im, Sung Heum Park, Hyosung Choi, Bo Ram Lee

Bibliographic record

VenueJournal of Materials Chemistry A · 2022
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Toronto
FundersSamsungBusan Metropolitan CityNational Research Foundation of Korea
KeywordsPerovskite (structure)Materials scienceLayer (electronics)DiodeOptoelectronicsPolymerCharge (physics)ElectronBinary numberMoleculeLight-emitting diodeElectron transport chainComposite materialChemistryPhysicsCrystallographyOrganic chemistry

Abstract

fetched live from OpenAlex

A polymer/small-molecule binary-blend hole transport layer provided balanced charge transport and efficient recombination of electrons and holes in the perovskite layer, and an optimal device based on the blended HTL shows the highest EQE of 5.30%.

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 score0.001
Threshold uncertainty score0.004

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)0.0010.000

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 head0.213
Teacher spread0.205 · 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

Citations28
Published2022
Admission routes1
Has abstractyes

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