MétaCan
Menu
Back to cohort
Record W1992310082 · doi:10.1002/app.24770

Aspects of foaming a glass‐reinforced polypropylene with chemical blowing agents

2006· article· en· W1992310082 on OpenAlexaff
Michael R. Thompson, Xingzong Qin, G. Zhang, Andrew N. Hrymak

Bibliographic record

VenueJournal of Applied Polymer Science · 2006
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMasterbatchBlowing agentMaterials sciencePolypropyleneComposite materialRheologyBlow moldingRheometerUltimate tensile strengthViscosityBreakageGlass fiberMolding (decorative)Nanocomposite

Abstract

fetched live from OpenAlex

Abstract This work explores the influence of a chemical blowing agent on different aspects of producing a short glass‐fiber‐reinforced polypropylene foam, examining the rheology of the system, the developed morphology of the part, and the resulting mechanical properties. Two different forms of an endothermic blowing agent, namely powder versus masterbatch, were compared to determine their effects on the process history and properties of an injection molded part. Samples were produced on an injection molding machine between 230 and 270°C using the low‐pressure foaming technique. Rheology of the resulting plasticized melt by the two different blowing agents was measured on an in‐line rheometer, showing a greater reduction in shear viscosity for the masterbatch additive, which correspondingly reduced the extent of fiber breakage observed. The final molded samples were analyzed for their foam structure (i.e., cell size, cell density, and skin thickness) as well as the properties of the glass fibers incorporated (namely, fiber length distribution). Tensile properties were found to diminish with increasing blowing agent content, though differences were observed based on the type of CBA used despite the similarities in foam structure produced. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 102: 4696–4706, 2006

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.573

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.006
GPT teacher head0.213
Teacher spread0.207 · 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
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Applied Polymer ScienceSame topicPolymer Foaming and CompositesFrench-language works237,207