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Record W2101316489 · doi:10.1109/tdei.2013.6571442

Membrane dielectric dispersion in nanosecond pulsed electroporation of biological cells

2013· article· en· W2101316489 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Dielectrics and Electrical Insulation · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Inactivation Methods
Canadian institutionsUniversity of Manitoba
FundersWestern Economic Diversification CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsNanosecondElectroporationDispersion (optics)DielectricMaterials scienceElectric fieldMembraneNonlinear systemRelaxation (psychology)OpticsOptoelectronicsChemistryPhysicsLaser

Abstract

fetched live from OpenAlex

In nanosecond pulsed electroporation of biological cells nanosecond duration pulses with high frequency spectral content are applied to the cell. We show that accurate modeling of the electroporation process on these time scales requires considering the effect of the dielectric dispersion on the electric potential across the membrane. We describe the dielectric relaxation of the membrane as dispersion in the timedomain and incorporate it with the nonlinear asymptotic model of electroporation. Our nonlinear dispersive model of a biological cell is solved using a finite element method enabling arbitrary cell structures and internal organelles to be modeled. Simulation results demonstrate two essential differences between dispersive and non-dispersive membrane models: the process of electroporation occurs faster when membrane dispersion is considered, and the required electric field to electroporate the cell is significantly reduced for the dispersive model.

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.

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 score0.166
Threshold uncertainty score0.803

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.250
Teacher spread0.235 · 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