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Record W2065108868 · doi:10.1002/marc.200600142

Electrically Conductive Poly(<scp>DL</scp>‐lactide)/Chitosan/Polypyrrole Complexes

2006· article· en· W2065108868 on OpenAlexaff
Ying Wan, Ya Fang, Zhilin Hu, Quan Wu

Bibliographic record

VenueMacromolecular Rapid Communications · 2006
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsPolypyrroleMaterials scienceMembraneChitosanPolymer chemistryMiscibilityChemical engineeringPercolation thresholdConductivityConductive polymerComposite materialPolymerElectrical resistivity and conductivityPolymerizationChemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract Summary: The fabrication of novel conductive poly( DL ‐lactide)/chitosan/polypyrrole complex membranes is reported. Using poly( DL ‐lactide)/chitosan blends as matrices and polypyrrole as a conductive component, several kinds of membranes with various compositions are prepared. A percolation threshold of polypyrrole as low as 1.8 wt.‐% is achieved for some membranes by controlling the chitosan proportion between 40 and 50 wt.‐%. SEM images exhibit that the membranes with a low percolation threshold show a two‐phase structure which consists of poly( DL ‐lactide) and chitosan phases. Dielectric measurements indicate that there is limited miscibility between the poly( DL ‐lactide) and chitosan but polypyrrole is nearly immiscible with the other two components. Based on the structural characteristics of the membranes, the polypyrrole particles are suggested to be localized at the interface between two phases. Dependence of conductivity of complex membranes on the PPy content. (○) PDLLA/PPy, (▪) PDLLA/ch(10)/PPy, (▵) PDLLA/ch(20)/PPy, (•) PDLLA/ch(30)/PPy, (□) PDLLA/ch(40)/PPy, and (▴) PDLLA/ch(50)‐PPy. magnified image Dependence of conductivity of complex membranes on the PPy content. (○) PDLLA/PPy, (▪) PDLLA/ch(10)/PPy, (▵) PDLLA/ch(20)/PPy, (•) PDLLA/ch(30)/PPy, (□) PDLLA/ch(40)/PPy, and (▴) PDLLA/ch(50)‐PPy.

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 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 score0.071
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.261
Teacher spread0.243 · 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.

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

Citations10
Published2006
Admission routes1
Has abstractyes

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