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Record W2811169180 · doi:10.1109/tcpmt.2018.2845847

Printing Green Nanomaterials for Organic Electronics

2018· article· en· W2811169180 on OpenAlexafffund
Yin Li, Manjusri Misra, Stefano Gregori

Bibliographic record

VenueIEEE Transactions on Components Packaging and Manufacturing Technology · 2018
Typearticle
Languageen
FieldEngineering
TopicNanomaterials and Printing Technologies
Canadian institutionsUniversity of Guelph
FundersOntario Ministry of Agriculture, Food and Rural Affairs
KeywordsPrinted electronicsMaterials scienceNanotechnologyNanomaterialsElectronicsContext (archaeology)NanocelluloseInkwellElectrical conductorOrganic electronicsFabricationConductive inkSubstrate (aquarium)Electrical engineeringSheet resistanceEngineeringComposite materialTransistorCelluloseChemical engineering

Abstract

fetched live from OpenAlex

Organic electronics have attracted increasing attention in recent years because of their large-scale production potential. In this context, inkjet printing as a scalable manufacturing process is well positioned for supporting the fabrication of organic electronics. In this paper, we propose a green substrate and functional inks based on bionanomaterials for fabricating dielectric and conductive layers using conventional inkjet technology. The feasibility of our approach is investigated by characterizing the properties of the conductive layers and capacitive structures and by demonstrating the functionality of a 1-D touch sensor. The printed conductive nanocarbon ink has a resistivity of 1.39 · 10-2Ω · m, and the printed nanocellulose ink achieves a relative permittivity of 4.39. The proposed green nanomaterials and printing technique are promising for manufacturing organic electronic devices with reduced costs and environmental footprint.

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: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

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 science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.012
GPT teacher head0.218
Teacher spread0.206 · 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

Citations16
Published2018
Admission routes2
Has abstractyes

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Same venueIEEE Transactions on Components Packaging and Manufacturing TechnologySame topicNanomaterials and Printing TechnologiesFrench-language works237,207