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Conductivity of inkjet-printed PEDOT:PSS-SWCNTs on uncoated papers

2012· article· en· W2319682773 on OpenAlexaff
Peter D. Angelo, Ramin Farnood, R. N. S. Sodhi, Gregory B. Cole

Bibliographic record

VenueNordic Pulp & Paper Research Journal · 2012
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsUniversity of WaterlooUniversity of Toronto
Fundersnot available
KeywordsMaterials sciencePEDOT:PSSComposite materialConductivityCarbon blackSoftwoodChemical engineeringPolymer chemistryLayer (electronics)ChemistryNatural rubber

Abstract

fetched live from OpenAlex

Abstract Poly(3,4-ethylenedioxythiopene): poly (styrene-sulfonate), or PEDOT:PSS, as well as singlewalled carbon nanotubes, were incorporated into an inkjet ink. Handsheets were prepared which contained varying amounts of TiO2filler, internal sizing agent, fixation agent, and either softwood or hardwood kraft pulp. The ink was jetted onto the handsheets to form conductive layers with apparent conductivity as high as 0.018 S/cm on internally alkyketene dimer-sized softwood kraft handsheets with no other additives. Internal sizing increased conductivity at low filler loadings by preventing PEDOT:PSS from penetrating into the substrate, resulting in a conductive ink film on the surface of the sample. Unsized handsheets allowed more rapid absorption, and therefore deeper penetration, of the PEDOT:PSS ink, which resulted in a more diffuse conductive layer. The inclusion of a polyethyleneimine retention aid for TiO2filler decreased conductivity significantly even in unfilled sheets by interaction with PSS-counterions. A positively charged fixation agent, poly(diallyldimethylammonium) chloride, reduced PEDOT conductivity through the retention of nonconductive PSS-anions.

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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.000

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 head0.133
GPT teacher head0.409
Teacher spread0.276 · 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

Citations11
Published2012
Admission routes1
Has abstractyes

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