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Record W2803281890 · doi:10.1063/1.5029380

Melt fracture of linear low-density polyethylenes: Die geometry and molecular weight characteristics

2018· article· en· W2803281890 on OpenAlexaff
Marzieh Ebrahimi, Tanja Tomković, Guochang Liu, Antonios A. Doufas, Savvas G. Hatzikiriakos

Bibliographic record

VenuePhysics of Fluids · 2018
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDie swellRheometryNewtonian fluidCapillary actionShear ratePolymerShear flowDie (integrated circuit)Composite materialLinear low-density polyethyleneRheologyMaterials scienceShear (geology)MechanicsPhysicsExtrusionNanotechnology

Abstract

fetched live from OpenAlex

The melt fracture phenomena of three linear low-density polyethylenes are investigated as a function of die geometry (capillary, slit, and annular) and molecular weight and its distribution. The onset of melt fracture instabilities is determined by using capillary rheometry, mainly studying the extrudate appearance using optical microscopy. It is found that the onset of flow instabilities (melt fracture phenomena) is significantly affected by die geometry and molecular weight characteristics of the polymers. Use of annular die eliminates the stick-slip transition (oscillating melt fracture) and delays the onset of sharkskin to higher values of shear rate and shear stress. Moreover, it is shown that the molecular weight characteristics of the polymers are well correlated with critical conditions for the onset of flow instabilities based on a criterion proposed in the literature [A. Allal et al., “Relationships between molecular structure and sharkskin defect for linear polymers,” J. Non-Newtonian Fluid Mech. 134, 127–135 (2006) and A. Allal and B. Vergnes, “Molecular design to eliminate sharkskin defect for linear polymers,” J. Non-Newtonian Fluid Mech. 146, 45–50 (2007)].

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 score0.150
Threshold uncertainty score0.652

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 head0.005
GPT teacher head0.221
Teacher spread0.216 · 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

Citations15
Published2018
Admission routes1
Has abstractyes

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