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Record W3215285195 · doi:10.1021/acsami.1c18939

Soft Polymer–Organolead Halide Perovskite Films for Highly Stretchable and Durable Photodetectors with Pt–Au Nanochain-Based Electrodes

2021· article· en· W3215285195 on OpenAlexafffund
Avi Mathur, Hua Fan, Vivek Maheshwari

Bibliographic record

VenueACS Applied Materials & Interfaces · 2021
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Research, Innovation and ScienceUniversity of WaterlooCanada Foundation for Innovation
KeywordsMaterials scienceElectrodeStretchable electronicsPolymerComposite materialOptoelectronicsViscoelasticityModulusPhotodetectorPerovskite (structure)BrittlenessElectronicsChemical engineering

Abstract

fetched live from OpenAlex

The rigid and brittle nature of methylammonium lead iodide (MAPbI 3 ) polycrystalline films limits their application in stretchable devices due to rapid deterioration in performance on cycling. By incorporation of polymer chains in the MAPbI 3 films, a strategy to alter the mechanical modulus and the viscoelastic nature of the films has been developed. Combining this with flexible nanochain electrodes, highly stretchable and stable perovskite devices have been fabricated. The resultant polymer-MAPbI 3 photodetector exhibits ultralow dark currents (∼10 –11 A) and high light switching ratios (∼10 3 ) and maintains 75% of performance after 30 days. The viscoelastic nature and lower modulus of the polymer improve the energy dissipation in the polymer-MAPbI 3 devices; as a result, they maintain 52% of the device performance after 10000 stretching cycles at 50% strain. The difference in the mechanical behavior is clearly observed in the failure mode of the two films. While rapid catastrophic cracking is observed in MAPbI 3 films, the intensity and size of such crack formation are highly limited in polymer-MAPbI 3 films, which prevent their failure.

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 categoriesMeta-epidemiology (narrow)
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 score0.001
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 head0.006
GPT teacher head0.190
Teacher spread0.185 · 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.

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

Citations22
Published2021
Admission routes2
Has abstractyes

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