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Record W2323214368 · doi:10.1149/1.3491277

Analytical Impedance Model for Electrochemically Driven Conducting Polymer Devices

2010· article· en· W2323214368 on OpenAlexaff
Tina Shoa, Dan Sik Yoo, Eddie C W Fok, John D. W. Madden

Bibliographic record

VenueECS Transactions · 2010
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceDielectric spectroscopyElectrical impedancePolypyrroleCapacitanceConductive polymerEquivalent circuitPolymerIonic conductivityElectrolyteIonic bondingElectrodeAnalytical Chemistry (journal)Composite materialElectrochemistryChemistryElectrical engineeringIonVoltage

Abstract

fetched live from OpenAlex

An analytical model is presented to describe the electrochemical impedance of conducting polymer based devices. The analytical expression of the impedance is obtained from a two dimensional finite transmission line equivalent circuit. The model relates impedance to cell geometry, electrolyte conductivity, polymer ionic and electronic conductivities and capacitance. These parameters were measured for a hexafluorophosphate (PF6-) doped polypyrrole material (the conducting polymer used in this study) and entered to the model to predict its impedance as a function of frequency. The model is unique in representing the two dimensional charging of the polymer, namely ionic mass transport through the thickness of the polymer structure and electronic resistance along its length. Close agreement is observed between impedance spectroscopy results and model prections of the charging of a polypyrrole film electrically connected at one end. The provides a means of modeling the electrochemical charging of conducting polymers and electrochemical double layer capacitor electrodes having significant ionic and electronic conductivities.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.306
Teacher spread0.266 · 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 designSimulation or modeling
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

Citations4
Published2010
Admission routes1
Has abstractyes

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Same venueECS TransactionsSame topicConducting polymers and applicationsFrench-language works237,207