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Record W1989552414 · doi:10.1177/1045389x12464530

A unified multiphysics finite element model of the polypyrrole trilayer actuation mechanism

2012· article· en· W1989552414 on OpenAlexaff
Aaron D. Price, Hani E. Naguib

Bibliographic record

VenueJournal of Intelligent Material Systems and Structures · 2012
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMultiphysicsActuatorFinite element methodPolypyrroleMaterials scienceMechanism (biology)Deformation (meteorology)Mechanical engineeringConductive polymerRange (aeronautics)Computer sciencePolymerStructural engineeringComposite materialEngineeringPhysics

Abstract

fetched live from OpenAlex

Conducting polymer materials have demonstrated new possibilities for low-density active material actuators. This article briefly introduces several existing conducting polymer actuator modelling approaches and identifies limitations on their sole applicability for predictive design. The main contribution of this article is the proposal and development of a new unified multiphysics finite element model of the polypyrrole trilayer actuation mechanism that does not depend on specimen-specific parameters. The model predicts the structural deformation of trilayer actuators using only material properties such that the model itself is sample-independent and thus may have practical use as an electroactive polymer design facility. Comparison with published data indicates that the model’s predictions fall within 95% confidence intervals throughout the entire range of input potentials evaluated.

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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.036
GPT teacher head0.267
Teacher spread0.231 · 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

Citations11
Published2012
Admission routes1
Has abstractyes

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