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Record W1595503587 · doi:10.1063/1.4917498

Onion artificial muscles

2015· article· en· W1595503587 on OpenAlexaff
Chien‐Chun Chen, Wen‐Pin Shih, Pei‐Zen Chang, Hsi‐Mei Lai, Shing‐Yun Chang, Pin‐Chun Huang, Huai-An Jeng

Bibliographic record

VenueApplied Physics Letters · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsUniversity of Waterloo
FundersMinistry of Science and Technology, Taiwan
KeywordsArtificial muscleElongationVoltageActuatorDeflection (physics)Contraction (grammar)Materials scienceBiophysicsElectric fieldPhysicsComposite materialElectrical engineeringOpticsBiologyEngineeringUltimate tensile strength

Abstract

fetched live from OpenAlex

Artificial muscles are soft actuators with the capability of either bending or contraction/elongation subjected to external stimulation. However, there are currently no artificial muscles that can accomplish these actions simultaneously. We found that the single layered, latticed microstructure of onion epidermal cells after acid treatment became elastic and could simultaneously stretch and bend when an electric field was applied. By modulating the magnitude of the voltage, the artificial muscle made of onion epidermal cells would deflect in opposing directions while either contracting or elongating. At voltages of 0–50 V, the artificial muscle elongated and had a maximum deflection of −30 μm; at voltages of 50–1000 V, the artificial muscle contracted and deflected 1.0 mm. The maximum force response is 20 μN at 1000 V.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.002

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 head0.021
GPT teacher head0.207
Teacher spread0.186 · 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

Citations16
Published2015
Admission routes1
Has abstractyes

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