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Record W2942496974 · doi:10.1002/smll.201900801

Halide Perovskite Nanocrystals for Next‐Generation Optoelectronics

2019· review· en· W2942496974 on OpenAlex
Maning Liu, Haichang Zhang, Dawit Gedamu, Paul Fourmont, Heikki Rekola, Arto Hiltunen, Sylvain G. Cloutier, Riad Nechache, Arri Priimägi, Paola Vivo

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSmall · 2019
Typereview
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsÉcole de Technologie Supérieure
FundersBusiness FinlandJane ja Aatos Erkon Säätiö
KeywordsHalideNanocrystalPerovskite (structure)Materials scienceOptoelectronicsNanotechnologyChemical engineeringInorganic chemistryChemistryCrystallography

Abstract

fetched live from OpenAlex

Colloidal perovskite nanocrystals (PNCs) combine the outstanding optoelectronic properties of bulk perovskites with strong quantum confinement effects at the nanoscale. Their facile and low-cost synthesis, together with superior photoluminescence quantum yields and exceptional optical versatility, make PNCs promising candidates for next-generation optoelectronics. However, this field is still in its early infancy and not yet ready for commercialization due to several open challenges to be addressed, such as toxicity and stability. Here, the key synthesis strategies and the tunable optical properties of PNCs are discussed. The photophysical underpinnings of PNCs, in correlation with recent developments of PNC-based optoelectronic devices, are especially highlighted. The final goal is to outline a theoretical scaffold for the design of high-performance devices that can at the same time address the commercialization challenges of PNC-based technology.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
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 head0.166
GPT teacher head0.314
Teacher spread0.148 · 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