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Record W2980446775 · doi:10.1063/1.5121632

Small-angle light scattering in large-amplitude oscillatory shear

2019· article· en· W2980446775 on OpenAlexaff
P. H. Gilbert, A. Jeffrey Giacomin

Bibliographic record

VenuePhysics of Fluids · 2019
Typearticle
Languageen
FieldChemistry
TopicSurfactants and Colloidal Systems
Canadian institutionsKingston Health Sciences CentreQueen's University
Fundersnot available
KeywordsScatteringPhysicsLight scatteringRayleigh scatteringScattering amplitudeShear (geology)OpticsStatic light scatteringBiological small-angle scatteringShear flowBreakupScattering theoryClassical mechanicsMolecular physicsMechanicsSmall-angle neutron scatteringNeutron scatteringMaterials scienceComposite material

Abstract

fetched live from OpenAlex

We explore wormlike micellar orientation during oscillatory shear using small-angle light scattering. Previous oscillatory-shear light scattering measurements focused on phase separation in polymeric solutions undergoing shear and none on wormlike micelles. We correlate light scattering videos of wormlike micelles undergoing oscillatory shear with molecular orientation. Specifically, we compare our orientation measurements with the predictions of rigid dumbbell theory. We find that “tulip” shaped scattering patterns caused by micellar orientation are only partially captured by the predicted scattering generated by rigid dumbbell theory. Additionally, we confirm that rigid dumbbell theory cannot describe the “butterfly” shaped scattering patterns arising from concentration fluctuations during micelle breakup. We successfully create a theory to describe both orientation and concentration fluctuation scattering by combining rigid rod Rayleigh-Debye scattering theory with flow induced Helfand-Fredrickson scattering theory.

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 score0.003

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.228
Teacher spread0.211 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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