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Record W3091614866 · doi:10.1039/d0cs00106f

Organic-based inverters: basic concepts, materials, novel architectures and applications

2020· review· en· W3091614866 on OpenAlexafffund
Tim Leydecker, Zhiming Wang, Fabrizio Torricelli, Emanuele Orgiu

Bibliographic record

VenueChemical Society Reviews · 2020
Typereview
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNational Key Research and Development Program of ChinaHigher Education Discipline Innovation ProjectRegione LombardiaNatural Sciences and Engineering Research Council of CanadaHorizon 2020 Framework ProgrammeUniversity of Electronic Science and Technology of China
KeywordsElectronic circuitInverterOLEDComputer scienceTransistorNanotechnologyOrganic moleculesElectronic engineeringLayer (electronics)Electrical engineeringMaterials scienceEngineeringChemistryVoltage

Abstract

fetched live from OpenAlex

While organic materials have demonstrated industry-leading performances in a wide array of electronic applications (including OLEDs and OPVs), their use for integration into electronic circuits has been so far limited, in spite of their potential for portable, flexible, light-weight, low-cost applications. However, recent advances in organic (semi)conductors and novel designs in organic field-effect transistors and hybrid systems have reaffirmed the potential of organic logic circuits. This review article provides an overview of organic-based inverter operation and considers all aspects of such circuits including their active layer, processing methods, hybrid organic/inorganic inverters, novel architectures and potential applications.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.024
GPT teacher head0.267
Teacher spread0.243 · 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 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

Citations74
Published2020
Admission routes2
Has abstractyes

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