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Record W2921370723 · doi:10.1002/pc.25257

Influence of glass fibers and rubber particles on the viscoelastic behavior of polyamide 6,6 blended composites

2019· article· en· W2921370723 on OpenAlexafffund
Adam Pearson, Tariq Sami Oweimreen, Hani E. Naguib

Bibliographic record

VenuePolymer Composites · 2019
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaOntario Centres of Excellence
KeywordsMaterials scienceComposite materialGlass transitionPolyamideGlass fiberCrystallizationThermoplasticNatural rubberComposite numberViscoelasticityPolymer

Abstract

fetched live from OpenAlex

Recently, in many industrial fields, there has been a drive to replace traditional metallic components with high strength polymer composites in the interest of reducing weight. Polyamide 6,6 (PA66) is a high strength thermoplastic and the behavior can be further enhanced by the addition of reinforcing fillers. Here, PA66 matrix has been reinforced with chopped glass fiber as well as hybrid composites containing both glass fiber and rubber particles. First, a baseline characterization of the mechanical properties is presented followed by a report on the thermal transitions, melting, crystallization, and glass transition, for the composite and pure materials. It is found that the filled samples show lower degree of crystallization and that the glass filled samples show lower crystallization and melting temperatures. Additionally, it was found that the glass reinforced samples led to a decrease in the glass transition temperature (Tg) while higher testing frequencies corresponded to an increase in Tg. Finally, it was demonstrated that the glass filled compounds were able to better retain their mechanical properties at high temperatures relative to the unreinforced materials. POLYM. COMPOS., 40:3960–3970, 2019. © 2019 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)0.000
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 score0.017
Threshold uncertainty score0.701

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 studies0.0000.001
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 head0.010
GPT teacher head0.218
Teacher spread0.208 · 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

Citations7
Published2019
Admission routes2
Has abstractyes

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