MétaCan
Menu
Back to cohort
Record W1988506695 · doi:10.1002/pen.20208

Rheological evaluation of metallocene polyethylenes with processing aids by multi‐wave oscillations

2004· article· en· W1988506695 on OpenAlexaff
Roland Vogel, Savvas G. Hatzikiriakos

Bibliographic record

VenuePolymer Engineering and Science · 2004
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsUniversity of British Columbia
FundersMax-Planck-Institut für Polymerforschung
KeywordsMaterials scienceRheologySlip (aerodynamics)ExtrusionComposite materialExtrusion mouldingBoron nitrideMelt flow indexHexagonal boron nitrideVolumetric flow ratePolymerMechanicsNanotechnologyCopolymerThermodynamics

Abstract

fetched live from OpenAlex

Abstract During the die flow of metallocene polyethylenes, flow instabilities may occur. Namely, wall slip, “sharkskin,” and stick‐slip (pressure oscillations) and gross fracture may be obtained depending on the volume flow rate and die geometry. It was reported that fluoroelastomers and boron nitride powders with hexagonal crystal structure can be used as suitable processing aids in melt extrusion processes. Fluoroelastomers at low concentrations act as die lubricants and may eliminate flow instabilities such as surface and stick‐slip melt fracture. On the other hand, specific boron nitride powders may not only eliminate surface and stick‐slip melt fracture, but also postpone gross melt fracture to higher volume flow rates. In this paper, a way for quantitative differentiation of the influence of polymer processing additives on rheological behavior is shown. Standard material functions show no clear‐cut differences. However, using multi‐wave oscillations with higher strain amplitudes make a quantitative assessment possible. Polym. Eng. Sci. 44:2047–2051, 2004. © 2004 Society of Plastics Engineers.

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.250
Teacher spread0.229 · 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
Published2004
Admission routes1
Has abstractyes

Explore more

Same venuePolymer Engineering and ScienceSame topicRheology and Fluid Dynamics StudiesFrench-language works237,207