MétaCan
Menu
Back to cohort
Record W2078968062 · doi:10.1021/ie0510932

Rheological Comparison of Chemical and Physical Blowing Agents in a Thermoplastic Polyolefin

2006· article· en· W2078968062 on OpenAlexafffund
Xingzong Qin, Michael R. Thompson, Andrew N. Hrymak, Agustín Torres

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2006
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsMcMaster University
FundersAUTO21 Network of Centres of ExcellenceNetworks of Centres of Excellence of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsViscosityPolyolefinThermoplasticBlowing agentRheologyMaterials scienceRheometerMasterbatchChemistryComposite materialChemical engineeringPolymer chemistry

Abstract

fetched live from OpenAlex

The influences of popular blowing agents (BAs), both chemical and physical types, in solution with a thermoplastic polyolefin (TPO) were investigated with an in-line capillary rheometer nozzle attached to a conventional reciprocating 55-ton injection molding machine. In the experiments, two types of masterbatch chemical BAs (endothermic and exothermic type) were dry mixed with the TPO resin and compared against two types of physical BAs (carbon dioxide and nitrogen) directly injected into a specially designed injection nozzle containing static mixer elements (SMX type). The effects of the main processing conditions (injection speed, pressure, temperature, and BA type and concentration) on the TPO melt rheology were studied. The viscosity reduction behaviors of chemical BAs and physical BAs were compared. The type of BA had a strong influence on the viscosity reduction behavior of the TPO melt, with the maximum viscosity reduction of 47% with 3.2 wt % CO 2 at 250 °C. Both physical and chemical BAs were found to successfully fit a previously developed viscosity model for single-phase gas−polymer solutions.

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 score0.508

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 integrity0.0000.001
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 head0.086
GPT teacher head0.348
Teacher spread0.262 · 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

Citations23
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueIndustrial & Engineering Chemistry ResearchSame topicPolymer Foaming and CompositesFrench-language works237,207