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Record W4388729828 · doi:10.1063/5.0168137

A new instrument for interfacial dilational rheology

2023· article· en· W4388729828 on OpenAlexafffund
Yun-Han Huang, John M. Frostad

Bibliographic record

VenueReview of Scientific Instruments · 2023
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsUniversity of British Columbia
FundersBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsRheologyMaterials scienceMechanicsComposite materialPhysics

Abstract

fetched live from OpenAlex

We present a new design for an interfacial dilational rheometer that can generate oscillatory dilational strain on a planar air-liquid interface. The strain is generated by a pneumatic mechanism involving a deformable film, which forms a circular barrier that can contract or expand under different pressures. The interfacial stress is measured using a Wilhelmy rod. We carefully examine and demonstrate the effects of potential sources of measurement error, including inertia, drag, buoyancy, flow from the bulk phase, and surface waves. The design avoids mixed deformations present in other instruments and is currently capable of accurate measurements at frequencies up to ∼0.1 Hz and dilational strains below 0.001, with potential for higher frequencies after further theoretical development. We demonstrate the integration of the interfacial dilational rheometer with a Langmuir trough by measuring the compression isotherm of an insoluble surfactant, stearic acid. Furthermore, we verify the capability of the interfacial dilational rheometer to perform frequency and amplitude sweeps and present the storage and loss moduli for a water-soluble surfactant, sodium dodecylbenzenesulfonate, at different concentrations.

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)0.001
metaresearch head score (Gemma)0.002
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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.026
GPT teacher head0.285
Teacher spread0.259 · 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
GenreMethods

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

Citations5
Published2023
Admission routes2
Has abstractyes

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