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Record W3008931405 · doi:10.1063/1.5145303

Capillary flow of sodium and zinc ionomers

2020· article· en· W3008931405 on OpenAlexafffund
Muaad Zuliki, Shiling Zhang, Tanja Tomković, Savvas G. Hatzikiriakos

Bibliographic record

VenuePhysics of Fluids · 2020
Typearticle
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCapillary actionSlip (aerodynamics)Composite materialExtrusionPolymerMaterials scienceZincThermodynamicsPhysicsMetallurgy

Abstract

fetched live from OpenAlex

The capillary flow properties of several commercial ionomers (sodium and zinc) were studied to assess their processability in terms of instabilities such as wall slip and melt fracture. Using capillary dies of various diameters and lengths to control capillary extrusion pressure, it was found that the viscosity of these polymers exhibits a relatively small dependence on pressure, more importantly at relatively smaller pressures. Using capillaries of various diameters at fixed length-to-diameter ratios, it was also found that the no-slip boundary condition is a valid assumption for these polymers due to the strong ionic associations and strong interactions with the capillary wall. All ionomers were found to exhibit gross melt fracture (no sharkskin), a phenomenon more dominantly observed at lower temperatures. The occurrence of gross melt fracture and the absence of surface (sharkskin) melt fracture is a characteristic of extensional strain-hardening polymers, noting that all ionomers examined exhibit this phenomenon. The critical shear stress for the onset of gross melt fracture was found to depend on the lifetime of associations, τS (τS∝ZE/ZS21/5, where ZE is the number of entanglements and ZS is the number of associations), independent of temperature, molecular weight, and type of ion (zinc or sodium).

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 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 score0.013
Threshold uncertainty score0.271

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.019
GPT teacher head0.227
Teacher spread0.208 · 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 teacher head, 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

Citations10
Published2020
Admission routes2
Has abstractyes

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