MétaCan
Menu
Back to cohort
Record W4417147419 · doi:10.1002/lpor.202502359

Interface Engineering for High‐Efficiency Perovskite Light‐Emitting Diode Through Poly(Vinyl Pyrrolidone) (PVP) Modification

2025· article· en· W4417147419 on OpenAlexaff
Xulan Xue, Chenhui Zhang, Huidan Zhang, Xingchen Lin, Yongqiang Ning, Lijun Wang, Wenyu Ji, Hongbo Zhu

Bibliographic record

VenueLaser & Photonics Review · 2025
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsMinistry of Education and Child Care
FundersNational Natural Science Foundation of China
KeywordsPerovskite (structure)CrystallizationWettingLayer (electronics)DiodeInterface (matter)Phase (matter)Kinetics

Abstract

fetched live from OpenAlex

ABSTRACT The performance of a perovskite light‐emitting diode (PeLED) is strongly dependent on the perovskite film quality. Among them, the buried interface plays a critical role in the crystallization dynamics of perovskite, directly determining the film morphology and optical properties. Herein, we demonstrate the buried interface engineering to regulate the crystallization kinetics of quasi‐2D perovskite film by inserting a poly(vinyl pyrrolidone) (PVP) modification layer between the hole transport layer and perovskite film. The strategy enhances the surface wettability of the hole transport layer, facilitating the uniform coverage of perovskite. Meanwhile, the carboxyl groups in PVP can interact with undercoordinated Pb ions in perovskite to increase the crystallization sites, thereby effectively passivating defects, regulating phase distribution, and suppressing the formation of low‐dimensional phases. Ultimately, a green PeLED treated with PVP modification layer has achieved a high external quantum efficiency of 24.2%, representing an 87% enhancement. Our results highlight the potential of interface engineering based on functional group interactions as an innovation strategy, enabling significant advances in PeLED efficiency.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.788
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.013
GPT teacher head0.271
Teacher spread0.258 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueLaser & Photonics ReviewSame topicPerovskite Materials and ApplicationsFrench-language works237,207