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Record W2561834718 · doi:10.22606/jan.2016.12007

Comparison of Three Polypyrrole-Cellulose Nanocomposites Synthesis

2016· article· en· W2561834718 on OpenAlexafffund
Benoît Bideau, Éric Loranger, Claude Daneault

Bibliographic record

VenueJournal of Advances in Nanomaterials · 2016
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersDivision of Materials ResearchFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsPolypyrroleMaterials scienceComposite materialNanocompositeThermogravimetric analysisNanofiberComposite numberScanning electron microscopeUltimate tensile strengthCelluloseElectrodeBacterial celluloseModulusPolymerChemical engineeringPolymerizationChemistry

Abstract

fetched live from OpenAlex

In this study, composite films based on TEMPO-oxidized cellulose nanofibers (TOCN), and polypyrrole (PPy) were synthesized by three different processes.The flexible composite films were investigated with scanning electron microscopy, thermogravimetric analysis, contact angle measurements, and finally, by mechanical and electrical testing.The developed composites have shown interesting mechanical properties (TOCN/PPy-3) as Young modulus (6.35 GPa) and tensile stress (65.6 MPa) or electrical conductivity (TOCN/PPy-1; 51.6 S/cm) for further applications such as flexible electrode.From proposed methods, the grafting of N-(3-aminopropyl)pyrrole was also interesting because it presented intermediate properties from all composites, and could represent a good compromise between the mechanical and electrical properties.Depending of their final application and by choosing an appropriate fabrication method, these composites could be considered in the design of high-performance electrodes for supercapacitor, battery, sensor or various packaging.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.028
GPT teacher head0.340
Teacher spread0.313 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2016
Admission routes2
Has abstractyes

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