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Record W4362602433 · doi:10.1021/acs.jpclett.3c00566

Ferroelectricity in Hybrid Perovskites

2023· review· en· W4362602433 on OpenAlexafffund
Matteo Manzi, Giovanni Pica, Michele De Bastiani, Soumya Kundu, Giulia Grancini, Makhsud I. Saidaminov

Bibliographic record

VenueThe Journal of Physical Chemistry Letters · 2023
Typereview
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Victoria
FundersCanada Foundation for InnovationNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsGovernment of Canada
KeywordsFerroelectricityMaterials sciencePyroelectricityPerovskite (structure)DielectricFigure of meritCurie temperaturePolarization (electrochemistry)OptoelectronicsNanotechnologyPiezoelectricityCondensed matter physicsChemistryFerromagnetismPhysicsCrystallographyComposite materialPhysical chemistry

Abstract

fetched live from OpenAlex

(PZT) are widely applied in many fields, from medical to aerospace, because of their dielectric, piezoelectric, and pyroelectric properties. In the past few years, hybrid organic-inorganic halide perovskites have gradually attracted attention for their optical and electronic properties, including ferroelectricity, and for their low fabrication costs. In this Review, we first describe techniques that are used to quantify ferroelectric figures of merit of a material. We then discuss ferroelectricity in hybrid perovskites, starting from controversies in methylammonium iodoplumbate perovskites and then focusing on low-dimensional perovskites that offer an unambiguous platform to obtain ferroelectricity. Finally, we provide examples of the application of perovskite ferroelectrics in solar cells, LEDs, and X-ray detectors. We conclude that the vast structure-property tunability makes low-dimensional hybrid perovskites promising, but they have yet to offer ferroelectric figures of merit (e.g., saturated polarization) and thermal stability (e.g., Curie temperature) competitive with those of conventional oxide perovskite ferroelectric materials.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.916
Threshold uncertainty score0.640

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 science0.0010.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.275
Teacher spread0.251 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations39
Published2023
Admission routes2
Has abstractyes

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