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Record W2097695892 · doi:10.1177/0021955x11420187

Evaluation of porous membrane core elasticity and porous morphology for polypyrrole trilayer actuators

2011· article· en· W2097695892 on OpenAlexaff
Aaron D. Price, T Gillen, CC Liu, C.L. O'Shaughnessy, HE Naguib

Bibliographic record

VenueJournal of Cellular Plastics · 2011
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials sciencePolypyrrolePolyvinylidene fluorideMembraneActuatorComposite materialPolymerDifluorideElectroactive polymersPorosityPolymerization

Abstract

fetched live from OpenAlex

Multilayer electroactive polymer actuators consisting of polypyrrole films electropolymerized on a passive polymer membrane core have been harnessed as a source of simple actuation. As an integral component of the actuator, the membrane plays a vital role in the transport of ionic species and largely dictates the stiffness of the layered configuration, yet in past studies the specification of the membrane has remained largely arbitrary. In this investigation, we use quasi-static and dynamic mechanical analysis to investigate the impact of the mechanical properties of the membrane on the actuation response of polypyrrole-based trilayer bending actuators. Candidate materials with distinctly varied microcellular morphologies are identified and include polyvinylidene difluoride, nylon, and nitrocellulose. The quasi-static stress-strain response and the frequency-dependent viscoelastic nature of the candidates are then evaluated. On the basis of mechanical properties these results indicate that polyvinylidene difluoride membranes are superior to the other candidates for application as trilayer actuator cores. Bis(trifluoromethane)sulfonimide doped polypyrrole actuators with polyvinylidene difluoride cores and nylon cores are then fabricated under various synthesis conditions and their electromechanical actuation behavior is reported.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.131
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.056
GPT teacher head0.250
Teacher spread0.193 · 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

Citations8
Published2011
Admission routes1
Has abstractyes

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