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Record W7133104284

Fiber-matrix Adhesion in Thermoplastic Composites

2022· dissertation· W7133104284 on OpenAlexafffund
Adam Pearson

Bibliographic record

VenueTSpace · 2022
Typedissertation
Language
FieldEngineering
TopicFiber-reinforced polymer composites
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsThermoplasticAdhesionGlass fiberPolymerAdhesiveFiberPolyesterPolyethyleneThermoplastic elastomer
DOInot available

Abstract

fetched live from OpenAlex

There has been a recent drive in many industries to replace legacy metallic components with lightweight fiber reinforced polymer composites. Thermoplastic matrix composites are becoming increasingly more prominent due to their inherent benefits such as the ability to be re-melted and re-shaped or recycled, fatigue and impact resistance, and corrosion resistance. Macromechanical properties of fiber reinforced polymers depend on a variety of factors from the mechanical properties of the fibers and matrix, the geometrical properties of the reinforcing fibers, voids and cracks in the composite, and the strength of the fiber-matrix adhesion. The fiber-matrix adhesion governs the effectiveness of the transfer of stress from the typically weak polymer matrix to the reinforcing fibers. Poor fiber-matrix interfacial shear strengths (IFSS) can lead to premature debonding of the fiber from the matrix which allows for the propagation of micro-cracks throughout the material, ultimately leading to failure. Various micromechanical tests, such as single-fiber pullout, have been developed to directly measure the adhesive strength between fibers and polymers. In this research, a novel single-fiber pullout test methodology has been utilized to measure the fiber-matrix adhesion for a variety of industrially relevant fiber reinforced thermoplastic composites. Carbon fiber proved to have greater adhesion to thermoplastic polyester elastomers than poly(p-phenylene-2,6-benzobisoxazole) (PBO) fibers due to their increased roughness and chemical functionality. Carbon content at higher binding energies of glass fiber sizings correlated to increased IFSS with polyketone matrices. Modification of high-density polyethylene (HDPE) with maleic anhydride is effective to improve adhesion to both carbon and glass fibers, and the effect of carbon fiber sizing to the IFSS was weaker than matrix modification. Surface texturing of carbon fiber with graphene nanoplatelets led to increased adhesion to HDPE matrices through mechanical interlocking effects due to increased fiber surface roughness. Fiber-matrix adhesion was found to decrease with increasing temperature due to the reduction of the compressive stresses at the fiber-matrix interface formed from polymer shrinkage during cooling. In general, the chemical properties of the matrix were the most critical factor to adhesion. The results of this research can be used to further the development of fiber reinforced thermoplastics.

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), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0270.002

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 head0.008
GPT teacher head0.289
Teacher spread0.281 · 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; both teacher heads agree on what is shown here.

Study designSimulation or modeling
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
Published2022
Admission routes2
Has abstractyes

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