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Record W2160481964 · doi:10.1021/acsami.5b09084

Efficient Luminescence from Perovskite Quantum Dot Solids

2015· article· en· W2160481964 on OpenAlexafffund
Younghoon Kim, Emre Yassitepe, Oleksandr Voznyy, Riccardo Comin, Grant Walters, Xiwen Gong, Pongsakorn Kanjanaboos, Ana F. Nogueira, Edward H. Sargent

Bibliographic record

VenueACS Applied Materials & Interfaces · 2015
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaKing Abdullah University of Science and TechnologyFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsPhotoluminescenceMaterials scienceNanocrystalPerovskite (structure)Quantum dotLuminescenceFabricationNanotechnologyQuantum yieldOptoelectronicsBand gapChemical engineeringOptics

Abstract

fetched live from OpenAlex

Nanocrystals of CsPbX3 perovskites are promising materials for light-emitting optoelectronics because of their colloidal stability, optically tunable bandgap, bright photoluminescence, and excellent photoluminescence quantum yield. Despite their promise, nanocrystal-only films of CsPbX3 perovskites have not yet been fabricated; instead, highly insulating polymers have been relied upon to compensate for nanocrystals' unstable surfaces. We develop solution chemistry that enables single-step casting of perovskite nanocrystal films and overcomes problems in both perovskite quantum dot purification and film fabrication. Centrifugally cast films retain bright photoluminescence and achieve dense and homogeneous morphologies. The new materials offer a platform for optoelectronic applications of perovskite quantum dot solids.

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 score0.002
Threshold uncertainty score0.005

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.019
GPT teacher head0.229
Teacher spread0.210 · 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

Citations552
Published2015
Admission routes2
Has abstractyes

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