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Record W2336131993 · doi:10.1117/12.2205905

Robotic extrusion processes for direct ink writing of 3D conductive polyaniline structures

2016· article· en· W2336131993 on OpenAlexafffund
F. Benjamin Holness, Aaron D. Price

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2016
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFabricationMaterials sciencePolyanilineFused filament fabricationExtrusionInkwell3D printingElectrical conductorFused deposition modelingNanotechnologyPolymerConductive polymerPlastics extrusionComposite materialPolymerization

Abstract

fetched live from OpenAlex

The intractable nature of intrinsically conductive polymers (ICP) leads to practical limitations in the fabrication of ICP-based transducers having complex three-dimensional geometries. Conventional ICP device fabrication processes have focused primarily on thin-film deposition techniques; therefore this study explores novel additive manufacturing processes specifically developed for ICP with the ultimate goal of increasing the functionality of ICP sensors and actuators. Herein we employ automated polymer paste extrusion processes for the direct ink writing of 3D conductive polyaniline (PANI) structures. Realization of these structures is enabled through a modified fused filament fabrication delta robot equipped with an integrated polymer paste extruder. This unique robot-controlled additive manufacturing platform is capable of fabricating high-resolution 3D conductive PANI and has been utilized to produce structures with a minimum feature size of 1.5 mm. The required processability of PANI is achieved by means of a counter-ion induced thermal doping method. Using this method, a viscous paste is formulated as the extrudate and a thermo-chemical treatment is applied post extrusion to finalize the complexation.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.135
Threshold uncertainty score0.740

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.018
GPT teacher head0.255
Teacher spread0.237 · 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.

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

Citations14
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicConducting polymers and applicationsFrench-language works237,207