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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 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 score0.208
Threshold uncertainty score0.487

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 head0.040
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 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

Citations4
Published2010
Admission routes1
Has abstractyes

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