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Record W3136796441 · doi:10.1117/12.2582732

Trilayer conducting polymer transduction: device physics, modeling, and simulation

2021· preprint· en· W3136796441 on OpenAlexaff
Sébastien Grondel, Sofiane Ghenna, Caroline Soyer, Éric Cattan, John D. W. Madden, Ngoc Tan Nguyen

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsUniversity of Northern British ColumbiaUniversity of British Columbia
FundersAgence Nationale de la Recherche
KeywordsMicroscale chemistryPEDOT:PSSMaterials scienceThermal conductionWork (physics)Conductive polymerPolymerIonic bondingIonic conductivityProcess (computing)NanotechnologyIonStatistical physicsChemical physicsComputer sciencePhysicsElectrodeThermodynamicsComposite materialElectrolyteMathematics

Abstract

fetched live from OpenAlex

Modelling trilayer conducting polymer is still challenging as it exhibits interrelated coupled multiscale and non-linear characteristics. Therefore, this work proposes to review the underlying electro-chemo-mechanical principles in ultrathin PEDOT trilayer ionic conducting polymers based upon internal ion charge transport, conduction phenomena, redox process and elastic deformation. Microscale governing equations are first analyzed and the choice of appropriate assumptions depending of the used material is discussed. Since exact analytical solutions can be so far given only for some limited conditions, numerical solutions are developed to solve the problem. Then simulations in both sensing and actuating are successfully compared with experiments.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.130
GPT teacher head0.339
Teacher spread0.209 · 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
GenreMethods

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

Citations0
Published2021
Admission routes1
Has abstractyes

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