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Record W2934617581 · doi:10.1117/12.2517969

Dual sensing and actuation of ultrathin conducting polymer transducers (Conference Presentation)

2019· preprint· en· W2934617581 on OpenAlexaff
Ngoc Tan Nguyen, Cédric Plesse, Frédéric Vidal, Sébastien Grondel, Éric Cattan, John D. W. Madden

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTransducerMaterials scienceVoltageCapacitive micromachined ultrasonic transducersActuatorPolymerDisplacement (psychology)OptoelectronicsAcousticsElectrical engineeringComposite materialPhysicsEngineering

Abstract

fetched live from OpenAlex

Conducting polymers are active materials that exhibit an interesting bidirectional electromechanical coupling, where an input voltage results in the displacement of the film and a voltage is produced when a displacement is applied to the film. Mechanical deformation of the transducer by external mechanical loads causes movement of ions, and the generation of voltages. In this work, a dual sensing and actuation model for conducting polymer is described. The model comprises an RC lumped circuit, representing the electrochemical model, a mechanical model described by a dynamic Euler – Bernoulli beam theory, and an empirical strain-to-charge ratio. All three submodels are presented in a self-consistent Bond Graph formalism. The predictions of this model are then demonstrated by comparing with the experimental sensing and actuation results of a 17 µm thin poly(3,4-ethylenedioxythiophene) – based trilayer transducer, showing that the complete electromechanical model elucidates an effective approach to describe both sensing and actuation.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.094
GPT teacher head0.316
Teacher spread0.222 · 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 designBench or experimental
Domainnot available
GenreOther

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
Published2019
Admission routes1
Has abstractyes

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