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Record W4411734141 · doi:10.1063/5.0272370

Time-dependent diffusion in one-dimensional disordered media decorated by permeable membranes: Theoretical findings backed by simulations and a new disorder class

2025· article· en· W4411734141 on OpenAlexaff
Magnus Herberthson, Peter J. Basser, Evren Özarslan

Bibliographic record

VenuePhysics of Fluids · 2025
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Mathematical Modeling in Engineering
Canadian institutionsEngineering Link (Canada)
FundersMilitary Traumatic Brain Injury InitiativeHenry M. Jackson FoundationNational Institute of Child Health and Human DevelopmentUniformed Services University of the Health SciencesEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentVetenskapsrådet
KeywordsThermal diffusivityMembraneDiffusionPhysicsSemipermeable membraneStatistical physicsAnomalous diffusionThermodynamicsMechanicsChemistryComputer scienceInnovation diffusion

Abstract

fetched live from OpenAlex

As the diffusion of fluids is hindered by semipermeable membranes, the long-time behavior of the diffusion coefficient is influenced by the arrangement of the membranes. We develop methods that predict this long-time instantaneous diffusivity from bulk diffusivity, the membranes' locations, and their permeabilities. We studied this problem theoretically and expressed the instantaneous diffusivity analytically as an infinite sum. An independent numerical scheme was employed. Several types of disorder in the membranes' positions were considered including a new disorder family that generalizes hyperuniform and short-range disorders. Our theoretical and numerical findings are in excellent agreement. Our methods provide an alternative means for studying time-dependent diffusion processes.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.238
Teacher spread0.228 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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