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Record W6987426274

Study on the Effect of Surface Energy of Polypropylene/Polyamide12 polymer Hybrid Matrix Reinforced with Virgin and Recycled Carbon Fiber

2017· dissertation· en· W6987426274 on OpenAlexfundno aff

Bibliographic record

VenueTSpace (University of Toronto) · 2017
Typedissertation
Languageen
FieldEngineering
TopicFiber-reinforced polymer composites
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaMitacsUniversity of TorontoFord Motor Company
KeywordsComposite numberWettingSurface energyAdhesionCarbon fibersMatrix (chemical analysis)Contact anglePolymer
DOInot available

Abstract

fetched live from OpenAlex

The presented work is focused on characterization of thermal treated recycled and virgin carbon fibers. Their thermal performances, chemical surface composition and its influence on interfacial adhesion phenomena on PP/PA12 hybrid matrix were compared using TGA, FTIR and XPS analysis. Additionally, differences between hybrid matrix structural performances of PP/PA12 using both surface modifiers PMPPIC and MAPP were investigated. Final mechanical properties improvements between 8% up to 17% were reached by addition of PMPPIC in PP/PA12 hybrid matrix. For PP/PA12 matrix reinforcement using virgin and recycled carbon fibers, impact energy was improved up to 98% compared with MAPP modified matrix leading to a novel composite with good energy absorption. Finally, wettability studies and surface free energy analysis of all materials studied support the effect of the addition of PMPPIC, MAPP and carbon fibers in final composite surface thermodynamics bringing important data correlation between\ninterfacial adhesion mechanisms and final composite performance.

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 categoriesMeta-epidemiology (narrow)
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 score0.179
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.005
GPT teacher head0.208
Teacher spread0.204 · 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.

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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

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