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Record W1978303689 · doi:10.1103/physreve.81.022901

Polarizability of red blood cells with an anisotropic membrane

2010· article· en· W1978303689 on OpenAlexaff
J.L. Sebastián, S. Muñoz, Miguel Sancho, Genoveva Martı́nez, K.V.I.S. Kaler

Bibliographic record

VenuePhysical Review E · 2010
Typearticle
Languageen
FieldMedicine
TopicBlood properties and coagulation
Canadian institutionsUniversity of Calgary
FundersComunidad de Madrid
KeywordsPolarizabilityAnisotropyDipoleElectric fieldMaterials scienceMembraneCondensed matter physicsDielectricDebyePermittivityField (mathematics)PhysicsOpticsChemistryQuantum mechanicsOptoelectronicsMathematics

Abstract

fetched live from OpenAlex

We predict the complex polarizability of a realistic model of a red blood cell (RBC), with an inhomogeneous dispersive and anisotropic membrane. In this model, the frequency-dependent complex electrical parameters of the individual cell layers are described by the Debye equation while the dielectric anisotropy of the cell membrane is taken into account by the different permittivities along directions normal and tangential to the membrane surface. The realistic shape of the RBC is described in terms of the Jacobi elliptic functions. To calculate the polarizability, we evoke the effective dipole moment method to determine the cell internal electric field distribution, employing an adaptive finite-element numerical approach. We have furthermore investigated the influence of the anisotropic membrane and dispersive electrical parameters of each individual cell layer on the total complex polarizability. Our findings suggest that the individual layer contribution depends on two factors: the volume of the layer and the associated induced electric field, which in turn is influenced by other layers of the cell. These results further show that the average polarizability spectra of the cell are significantly impacted by the anisotropy and associated dispersion of the cellular compartments.

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 score0.042
Threshold uncertainty score0.211

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 head0.015
GPT teacher head0.276
Teacher spread0.261 · 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

Citations4
Published2010
Admission routes1
Has abstractyes

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