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Record W2057684921 · doi:10.3139/217.2855

Effect of Feeding Strategy on the Mechanical Properties of PP/Recycled EPDM/PP-G-MA Blends

2014· article· en· W2057684921 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Polymer Processing · 2014
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMaterials sciencePolypropyleneMaleic anhydrideComposite materialIzod impact strength testPlastics extrusionExtrusionCompoundingUltimate tensile strengthThermoplasticScanning electron microscopeThermoplastic elastomerElastomerPolymer blendPolymerCopolymer

Abstract

fetched live from OpenAlex

Abstract In this work, thermoplastic elastomers based on polypropylene (PP) and recycled ethylene-propylene-diene monomer (r-EPDM) blends with polypropylene-graft-maleic anhydride (PP-g-MA) as modifier (0 to 8 wt.%) were prepared by melt compounding via twin-screw extrusion followed by injection molding. In particular, the effects of material composition and feeding strategies were studied. The morphological and mechanical properties of the blends were investigated by using scanning electron microscopy (SEM), tensile, flexural, impact, density and hardness tests. The results indicate that good dispersion and compatibility between PP and r-EPDM particles was obtained and that incorporation of r-EPDM leads to significant increase in PP impact strength. Finally, the feeding order of each component in the extruder was found to modify substantially the final morphology of the blend, thus having a direct influence on their mechanical properties.

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.

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 score0.025
Threshold uncertainty score0.388

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 head0.023
GPT teacher head0.264
Teacher spread0.241 · 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