MétaCan
Menu
Back to cohort
Record W2120173111 · doi:10.1002/cjce.22197

Enhancement of electric properties of polypyrrole by copper electrodeposition

2015· article· en· W2120173111 on OpenAlexvenueno aff
Imene Chikouche, Ali Sahari, Ahmed Zouaoui, Sophie Tingry

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2015
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsnot available
FundersMinistère de l'Education Nationale, de l'Enseignement Superieur et de la Recherche
KeywordsPolypyrroleCopperMaterials scienceElectrolysisConductivityScanning electron microscopeConductive polymerComposite materialChemical engineeringPolymer chemistryElectrodeMetallurgyChemistryPolymerizationPolymerElectrolyte

Abstract

fetched live from OpenAlex

Polypyrrole films (0.2–1.0 μm thick) were electrosynthetized in organic media by potentiodynamic electropolymerization on silicon surfaces. To improve the electronic conductivity of the polypyrrole films, elemental copper was electrodeposited directly on the film surface by simple electrolysis from a copper chloride bath. Copper electrodeposited onto the surface of the polypyrrole film was characterized by X‐ray diffraction and consisted only of Cu fcc phase. The presence of copper on the PPy surface did not greatly affect the overall electronic conductivity of the material. However, immersion of polypyrrole films in Cu 2+ solution for a period of time caused Cu 2+ ions to enter the polypyrrole matrix. The reduction of the Cu 2+ after insertion into the polypyrrole template formed a polypyrrole/Cu composite with high electrical conductivity; this conductivity was higher for longer steeping (immersion) time. Raman spectroscopy shows much greater peak intensities when copper was present in the polypyrrole matrix. Scanning electron microscopy and cross‐sectional analysis showed clear differences in the appearance of the films with copper deposited onto the polypyrrole surface and copper inserted into the polypyrrole matrix.

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 score0.002
Threshold uncertainty score0.186

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 head0.013
GPT teacher head0.193
Teacher spread0.179 · 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

Citations13
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicConducting polymers and applicationsFrench-language works237,207