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Rate Limits in Conducting Polymers

2008· article· en· W2025841894 on OpenAlexaff
Tina Shoa, John D. W. Madden, Chi Wah Eddie Fok, Tissaphern Mirfakhrai

Bibliographic record

VenueAdvances in science and technology · 2008
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsActuatorMaterials scienceCapacitanceElectrolyteVoltageDiffusionCarbon nanotubeLimit (mathematics)Control theory (sociology)Work (physics)Limiting currentPolymerNanotechnologyMechanicsElectrical engineeringElectronic engineeringMechanical engineeringComposite materialElectrodeThermodynamicsComputer scienceEngineeringPhysicsElectrochemistry

Abstract

fetched live from OpenAlex

Conducting polymer actuators are of interest in applications where low voltage and high work density are beneficial. These actuators are not particularly fast however, with time constants normally being greater than 1 second. Strain in these actuators is proportional to charge, with the rate of charging being found to limit the speed of actuation. This rate of charging is in turn limited by a number of factors, the dominant factor depending on the actuator and cell geometry, the potential range, the composition and the timescale of interest. Mechanisms that slow response can be as simple as the RC charging time arising from the actuator capacitance and the series resistances of the electrolyte and the contacts, or may involve polymer electronic or ionic conductivities, which can in turn be functions of potential. Diffusion can also be a factor. An approach is presented to help estimate the relative magnitudes of these rate limiting factors, thereby enabling actuator designs to evaluated and optimized for a given application. The general approach discussed is also useful for analyzing rate limits in carbon nanotube actuators and other related technologies.

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)0.002
metaresearch head score (Gemma)0.013
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0030.008
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.002

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 head0.314
Teacher spread0.278 · 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
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

Citations28
Published2008
Admission routes1
Has abstractyes

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