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Record W2031059737 · doi:10.1002/pen.21452

Evaluating the processability of film blowing resins

2009· article· en· W2031059737 on OpenAlexafffund
Jen Shueng Tiang, John M. Dealy

Bibliographic record

VenuePolymer Engineering and Science · 2009
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaPolytechnique Montréal
KeywordsMaterials scienceRheometerRheologyExtensional definitionComposite materialBlow moldingPolymerBranching (polymer chemistry)PolyethyleneBlowing agentExtensional viscosityBubbleCompoundingPolymer scienceComputer science

Abstract

fetched live from OpenAlex

Abstract A key factor that limits the production rate in film blowing is bubble instability. Although processing conditions play an important role, it is well known that some resins are more resistant to instabilities than others. It is clear that long‐chain branching enhances stability, but it is not currently possible to model the process with sufficient accuracy to establish a quantitative relationship between rheological properties and blown film processability. It would thus be useful to be able to compare the processabilities of film resins by means of a laboratory measurement. We compared two laboratory tests that may be helpful in evaluating the ability of a resin to resist instabilities in the film blowing process. One of these was a film resin tester designed to simulate some aspects of the film blowing process, and the other was an extensional rheometer. We used a set of polyethylene resins that had been used previously in an extensive study of blown film stability. The extensional rheometer clearly shows the superiority of low‐density polyethylene but is not able to distinguish among polymers of other types. The melt tester, on the other hand, is sensitive to differences among linear polymers. POLYM. ENG. SCI., 2009. © 2009 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 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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.904
Threshold uncertainty score0.236

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.290
Teacher spread0.272 · 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

Citations3
Published2009
Admission routes2
Has abstractyes

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