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Record W2083366895 · doi:10.1115/imece2009-11289

Development and Characterization of Novel Conductive Polypyrrole-Polylactide Open-Porous Composites

2009· article· en· W2083366895 on OpenAlexaff
Christine Chan, Weijia Zhang, Hani E. Naguib

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPolypyrroleMaterials scienceComposite materialPolymerizationPorosityCompression moldingPolymerIn situ polymerizationComposite numberConductive polymerCompressive strengthCuring (chemistry)Coating

Abstract

fetched live from OpenAlex

Novel polypyrrole-polylactide blends were fabricated and characterized using compression molding, salt leaching, and in situ polymerization. Open-porous polylactide samples were fabricated using compression molding and salt leaching techniques with varying salt-to-polymer mass ratios of 3:1, 6:1, and 9:1. The samples then underwent in situ polymerization of pyrrole and iron (III) chloride to obtain a uniform coating of polypyrrole. Characterization of these novel composites comprised of their physical, mechanical, and electrical properties. With increasing salt-to-polymer mass ratio, it was found that the relative density decreased, the open porosity increased while pore size and pore density generally remained independent. The polypyrrole coating did not have a significant effect on the structure of the pore network. Microscopic polypyrrole nodules were observed to be uniformly coated on the surface and sub-surface of the composites. The compressive modulus decreased with increasing salt-to-polymer mass ratios. In addition, the modulus of the coated 3:1 salt-to-polymer mass ratio sample was twice the value obtained for the uncoated sample while the modulus values for the 6:1 and 9:1 samples did not significantly change. The conductivity increased as the salt-to-polymer mass ratio increased. The relationships observed between the structure and resulting properties provided the basis for future development and characterization of these novel porous composites.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.024
Threshold uncertainty score0.294

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
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 head0.040
GPT teacher head0.285
Teacher spread0.244 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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