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Record W3209460076 · doi:10.1021/acsaem.1c02577

Photodeposited Polyamorphous CuO <sub> <i>x</i> </sub> Hole-Transport Layers in Organic Photovoltaics

2021· article· en· W3209460076 on OpenAlexafffund
Nicholas M. Randell, Renaud Miclette Lamarche, Franceso Tintori, Roman Chernikov, Gregory C. Welch, Simon Trudel

Bibliographic record

VenueACS Applied Energy Materials · 2021
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsCanadian Light Source (Canada)University of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesCanada Foundation for InnovationCanadian Light Source
KeywordsMaterials scienceThin filmAmorphous solidFabricationAnnealing (glass)Organic solar cellPhotovoltaicsNanotechnologyOrganic semiconductorPEDOT:PSSOptoelectronicsChemical engineeringPhotovoltaic systemPolymerLayer (electronics)Composite materialElectrical engineeringChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Hole-selective charge-transport layers are an important part of modern thin-film electronics, serving to direct electron flow and prevent leakage current. Crystalline metal-oxide hole-transport layers (HTLs) such as NiO and CuOx exhibit high performance and stability. However, they are traditionally not amenable to scalable and sustainable solution-processing techniques. Conversely, amorphous metal oxides are much more readily prepared by low-temperature solution processing methods but often lack the charge-transport properties of crystalline semiconductors. Herein, we report the fabrication of amorphous a-CuOx thin films from commercially available starting materials using a simple UV-based thin-film deposition method. Subsequent thermal annealing of the a-CuOx induces an amorphous-to-amorphous phase transition, resulting in p-type semiconducting behavior. The resulting thin films were used as HTLs in organic photovoltaic devices with power conversion efficiencies comparable to those fabricated with PEDOT:PSS.

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 score0.003
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)0.0010.000

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 head0.172
Teacher spread0.167 · 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

Citations9
Published2021
Admission routes2
Has abstractyes

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