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Record W4367627784 · doi:10.3390/sym15051004

Fluctuating Flexoelectric Membranes in Asymmetric Viscoelastic Media: Power Spectrum through Mechanical Network and Transfer Function Models

2023· article· en· W4367627784 on OpenAlexafffund
E. E. Herrera‐Valencia, Alejandro D. Rey

Bibliographic record

VenueSymmetry · 2023
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsMcGill University
FundersDirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de MéxicoUniversidad Nacional Autónoma de MéxicoCompute CanadaNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsViscoelasticityRheologyMechanicsAsymmetryRigidity (electromagnetism)Materials scienceWork (physics)Elasticity (physics)Flow (mathematics)MembraneClassical mechanicsPhysicsComposite materialThermodynamicsChemistry

Abstract

fetched live from OpenAlex

Flexoelectric liquid crystalline membranes immersed in asymmetric viscoelastic media is a material system model with physiological applications such as outer hair cells (OHCs), where membrane oscillations generate bulk flow. Motivated by this physiological process, here we extend our previous work by characterizing the force transmission output of our model in addition to viscoelastic fluid flow, since solid–fluid interactions are an essential feature of confined physiological flow and flow in immersed elastic structures. In this work, the rigidity of the confinement results in a passive force reception, while more complete solid–fluid interactions will be considered in the future. A significant contribution of this work is a new asymmetry linear viscoelastic electro-rheological model and the obtained implicit relation between force transmission and flow generation and how this relation is modulated by electric field frequency and the material properties of the device. Maximal force and flow are found at resonant frequencies of asymmetry viscoelastic bulk phases, flexoelectric and dispersion mechanisms through the elastic and Womersley numbers.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.893
Threshold uncertainty score0.799

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
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 head0.010
GPT teacher head0.206
Teacher spread0.196 · 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 designSimulation or modeling
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

Citations3
Published2023
Admission routes2
Has abstractyes

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