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Record W2228701082 · doi:10.1149/ma2015-02/33/1210

Hybrid Perovskites: A New Class of Compound Semiconductors with Unique Properties

2015· article· en· W2228701082 on OpenAlexaff
Tom Wu

Bibliographic record

VenueECS Meeting Abstracts · 2015
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsAmbipolar diffusionPerovskite (structure)Materials scienceNanotechnologySemiconductorBand gapOptoelectronicsCharge carrierChemistryElectron

Abstract

fetched live from OpenAlex

Organometallic hybrid perovskites have recently attracted enormous attention for photovoltaic, light emitting and other applications. Combining the advantages of both organic and inorganic materials, hybrid perovskites possess a wide range of extraordinary properties, such as tunable band gap, long carrier diffusion length, and ambipolar charge transport. Here, we will discuss first the physics of this interesting class of highly processable materials such as phase transitions and dielectric properties. Various solution and vapor based synthesis routes have been introduced to produce perovskite thin films and nanostructures. Regarding device applications, we will discuss the performance of perovskite solar cells using nano oxides such as SrTiO3, Zn2SnO4, and Cu2O as charge transporting materials. These versatile hybrid materials also found application in electronic devices. In the last part, we will discuss the applications of light sensitive perovskites in ambipolar phototransistors and nonvolatile resistive switching memories.

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 score0.003
Threshold uncertainty score0.009

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.033
GPT teacher head0.219
Teacher spread0.186 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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