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Record W2054561151 · doi:10.1103/physreve.65.061511

Intermembrane spacing and velocity profiling of a lamellar lyotropic complex fluid under flow using x-ray diffraction

2002· article· en· W2054561151 on OpenAlexafffund
Sarah E. Welch, MacKenzie R. Stetzer, Gang Hu, E. B. Sirota, Stefan H. J. Idziak

Bibliographic record

VenuePhysical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · 2002
Typearticle
Languageen
FieldChemistry
TopicElectrostatics and Colloid Interactions
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Department of Energy
KeywordsLaminar flowMechanicsMaterials scienceCouette flowShear rateSimple shearNewtonian fluidShear flowShear (geology)Capillary actionShear stressPhysicsFlow (mathematics)RheologyComposite material

Abstract

fetched live from OpenAlex

We report on the use of x-ray diffraction as a means of extracting velocity profiles from a non-Newtonian complex fluid under laminar flow. In particular, we applied this technique to a concentrated undulating membrane system flowing through a cylindrical capillary tube. The intermembrane separation d was measured as a function of simple shear using a Couette flow cell. A logarithmic dependence of d as a function of the shear rate was observed, while there was a linear relationship between the fractional intermembrane spacing and the shear stress. Subsequent measurement of the system's intermembrane spacing as a function of position within the cylindrical flow pipe allowed for the calculation of a shear-rate profile within the capillary. Simple numerical integration then yielded an accurate velocity profile of the fluid flowing through the pipe. Both shear thickening and plug flow shear thinning profiles were observed.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.024
GPT teacher head0.308
Teacher spread0.285 · 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

Citations6
Published2002
Admission routes2
Has abstractyes

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