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Understanding on the formation mechanisms of quasi-2D Ruddlesden- Popper halide perovskites

2022· article· en· W4303183295 on OpenAlexaff
Yerang Park, Inchan Hwang

Bibliographic record

VenueCeramist · 2022
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Toronto
FundersNational Research Foundation of KoreaKwangwoon University
KeywordsPerovskite (structure)HalideNucleationMaterials sciencePhotovoltaicsThin filmDiodeOptoelectronicsLayer (electronics)Chemical physicsNanotechnologyCrystallographyChemistryInorganic chemistryPhotovoltaic systemPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Two dimensional (2D) or quasi-2D halide perovskites have attracted intense interest in recent years for the replacement of three dimensional(3D) perovskites, owing to their stability and structural/optoelectronic tunability. However, the control of their composition and orientation is challenging when solution processed for thin films, because of the similarity in formation energy for different layer numbers. In this review, we discuss the factors affecting formation of 2D and quasi 2D perovskites, especially Ruddlesden-Popper perovskites, in solution-processed thin films. All the factors are associated with nucleation and growth kinetics, which affect their phase distribution and orientation. Furthermore, the performances of the photovoltaics and light-emitting diodes based on the controlled quasi-2D perovskites are briefly discussed.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
Threshold uncertainty score0.573

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)0.0010.000

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 head0.064
GPT teacher head0.219
Teacher spread0.155 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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