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Record W2506653466 · doi:10.1177/026248930802700401

Compression Moulding of Polypropylene Foams and Their Properties

2008· article· en· W2506653466 on OpenAlexafffund
Ahmed Mechraoui, Mohamad Reza Barzegari, Bernard Riedl, Denis Rodrigue

Bibliographic record

VenueCellular Polymers · 2008
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBlowing agentMaterials scienceComposite materialPolypropyleneUltimate tensile strengthCell sizeCompression (physics)Syntactic foamFlexural strengthInjection mouldingCompression moldingPolyurethaneMold

Abstract

fetched live from OpenAlex

Compression moulding was used to produce polypropylene foams using azodicarbonamide as a chemical blowing agent. The morphology of the foams is reported in terms of cell size, cell density, and skin thickness using 1.5, 2, 2.5 and 3% of blowing agent. The resulting foams were characterized mechanically in terms of tensile and flexural moduli and comparison with mechanical models was made to predict foam performance. In general, mechanical properties, cell density, skin thickness and foam density decreased, while cell size increased with increasing amount of blowing agent. It was also found that simple mechanical models based solely on foam density and skin thickness were not able to correctly predict all the experimental data. For better prediction, foam density profile must be used.

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)0.000
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 score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.189
Teacher spread0.168 · 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

Citations10
Published2008
Admission routes2
Has abstractyes

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