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Record W4416879915 · doi:10.37665/jsmtlkwuw50812

Stretchable Polyurethane-Based Conductive Ink for E-Textile Applications

2019· article· W4416879915 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Surface Mount Technology · 2019
Typearticle
Language
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsElectrical conductorInkwellCoatingConductive inkTextileConductive polymerPorosityDurabilityPrinted electronics

Abstract

fetched live from OpenAlex

ABSTRACT E-textiles play an important role in wearable electronics such as sensor, supercapacitor and nanogenerator applications. Coating or printing conductive ink on textiles is a simple, inexpensive and large-scale manufacturing method. However, to obtain highly conductive conductors, most conductive inks that are metal-based and carbon-based suffer from their poor adhesion to the textile; the cured inks are prone to be wiped off and washed away. Some inks on textiles crack because of the rigid property of their binder and the porous and deformable structure of the textile. In addition, organic solvents are often used in conductive inks which are harmful to the environment and human body. In this paper, a new aqueous-based conductive ink, which can penetrate into the textile effectively and obtain high conductivity, high stretchability and high durability is described and analyzed. This ink uses a highly stretchable water-based polyurethane as a binder. It is demonstrated that a textile coated with this ink can maintain high conductivity after 10 stretch cycles and 20 wash cycles. A strain E-textile sensor on the human body shows the potential application for tracking movement.

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.

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 score0.446
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.011
GPT teacher head0.244
Teacher spread0.233 · 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