MétaCan
Menu
Back to cohort
Record W2400426840 · doi:10.1002/app.43759

Effects of oxidant and dopants on the properties of cellulose/PPy conductive composite hydrogels

2016· article· en· W2400426840 on OpenAlexaff
Liangjing Fang, Lihong Zhao, Xiangtao Liang, Huining Xiao, Liying Qian

Bibliographic record

VenueJournal of Applied Polymer Science · 2016
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsPolypyrroleSelf-healing hydrogelsPolymerizationDopantMaterials scienceComposite numberConductive polymerChemical engineeringPolymer chemistryCelluloseConductivityComposite materialPolymerDopingChemistry

Abstract

fetched live from OpenAlex

ABSTRACT Cellulose/Polypyrrole (PPy) composite hydrogels were prepared by in situ chemically oxidative polymerization of pyrrole in the cellulose matrix. Ferric chloride (FeCl 3 ) was used as an oxidant and four sulfonic compounds were used as dopants in order to investigate the effects on the properties of cellulose/PPy conductive composite hydrogels. The extent of polymerization of PPy was determined by the amount of the oxidant and the composite hydrogels with oxidant at 0.3 M −0.5 M exhibited the higher conductivities for the intrachain and interchain conductivities of conductive polymers; the fracture stress of the composite hydrogels could be up to 26.25 MPa with a strain of 86.8% when the oxidant was at 0.5 M . Doping is an efficient way to improve the conductivity of the composite hydrogels and four kinds of dopant were compared in this work. Long alkane chain and side group in dopants can increase the steric hindrance of PPy polymerization which resulted in the lower conductivity of the composite hydrogels compared to dopants with smaller steric hindrance. The conductivity of the composite hydrogel firstly increased and then decreased with the concentration of dopants from 0.1 M to 1.0 M in this work. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133 , 43759.

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)0.000
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 score0.006
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.016
GPT teacher head0.224
Teacher spread0.208 · 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

Citations24
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Applied Polymer ScienceSame topicConducting polymers and applicationsFrench-language works237,207