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Record W2129159906 · doi:10.1149/1.3489932

A Model for Mechanical Force Sensing in Conducting Polymers

2010· article· en· W2129159906 on OpenAlexaff
Tissaphern Mirfakhrai, Tina Shoa, Niloofar Fekri, John D. W. Madden

Bibliographic record

VenueECS Transactions · 2010
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCapacitanceElectrolyteMaterials sciencePolymerVoltagePolypyrroleConductive polymerElectrodeWork (physics)Aqueous solutionMechanicsComposite materialChemistryThermodynamicsElectrical engineeringPhysicsPolymerizationPhysical chemistry

Abstract

fetched live from OpenAlex

Conducting polymers have been shown to work as force sensors in an electrolyte by generating a voltage or current when the force applied to them is changed. One possible sensing mechanism is the change in the polymer capacitance as a result of an induced tension. This change in capacitance can be related to perturbation of Donnan equilibrium potential by external load. In this paper we present a model using this proposition to predict conducting polymer sensing behaviour. The model also takes the dependence of the sensing voltage on the polymer oxidation state into account. The predicted model results are compared with experimental measurements on a free-standing film of polypyrrole in an aqueous electrolyte of NaPF6. Sensing voltages ranging from ~ 80-110 μV were detected in response to 1.72 MPa change in the polymer stress, when the polymer was at oxidation states of -0.2 V to 0.4 V vs. Ag/AgCl reference electrode. The load-dependent capacitance necessary to induce the observed sensing currents and voltages is determined. The cycle life of the polypyrrole mechanical sensor was also studied and it was shown that after an initial transient period, the sensing current amplitude stays constant for at least 5400 cycles.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.345
Threshold uncertainty score0.399

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.066
GPT teacher head0.304
Teacher spread0.238 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

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