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Record W2944756674 · doi:10.1039/c9ra01751h

Effects of membrane thickness on the performance of ionic polymer–metal composite actuators

2019· article· en· W2944756674 on OpenAlexaff
Chungik Oh, Suran Kim, Hongjun Kim, Gun Park, Jaegyu Kim, Jeongjae Ryu, Panpan Li, Sunghwan Lee, Kwangsoo No, Seungbum Hong

Bibliographic record

VenueRSC Advances · 2019
Typearticle
Languageen
FieldEngineering
TopicDielectric materials and actuators
Canadian institutionsKootenay Association for Science & Technology
FundersMinistry of Science and ICT, South KoreaKorea Advanced Institute of Science and TechnologyNational Research Foundation of KoreaNational Research Foundation
KeywordsNafionMaterials scienceComposite materialCrystallinityIonic bondingComposite numberActuatorCantileverElastic modulusMembranePolymerMicroprobeBendingUltimate tensile strengthAnnealing (glass)ElectrodeChemistryIonElectrochemistry

Abstract

fetched live from OpenAlex

In this study, we report the effects of Nafion thickness on the performance of ionic polymer-metal composite (IPMC) actuators. We analyzed the actuation properties of the IPMC actuators, such as displacement and tip force, under external voltage, as a function of their thickness. In order to understand the relationship between thickness and actuation properties, we developed a semi-quantitative model of voltage induced ionic diffusion and its contribution to bending of the Nafion cantilever. Furthermore, we investigated the mechanical properties of the Nafion membranes at sub-micro scale as well as bulk scale, using atomic force microscopy (AFM) and tensile test. The results of the two methods indicated opposite trends of elastic modulus and crystallinity as a function of thickness. We hypothesized that the hot-pressed Nafion was composed of three layers with different crystallinity. Our results suggest that for a high performance IPMC actuator, we need better control of the annealing temperature gradient.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.003
GPT teacher head0.183
Teacher spread0.181 · 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

Citations24
Published2019
Admission routes1
Has abstractyes

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