Simultaneous reinforcement of matrix and fibers for enhancement of mechanical properties of graphene‐modified laminated composites
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Bibliographic record
Abstract
Fiber and matrix‐dominant properties of fiber‐reinforced polymer composites are important in many structural applications. Pre‐mixing the polymer matrix with nanoparticles could enhance the through‐thickness or matrix‐dominant properties. Surface treatment of fiber reinforcements with nanoparticles, on the other hand, could improve the in‐plane or fiber‐dominated properties of laminated composites, as well as interfacial adhesion. In this article, a novel low‐cost manufacturing method, combining surface treatment of fibers with matrix modification techniques, was devised to incorporate nanoparticles for the simultaneous reinforcement of matrix and fibers without increasing the viscosity of the epoxy matrix. Several graphene‐based nanomaterials, including graphene oxide (GO) and its thermally reduced version (rGO), graphene nanoplatelets (GNPs), and multi‐walled carbon nanotubes (MWCNTs) were employed to modify the epoxy matrix and the surface of glass fibers. Unmodified and modified epoxy and fibers were used for fabricating multiscale glass fiber‐reinforced composites following the vacuum‐assisted resin transfer molding process. The composites obtained combined improvements in both the fiber and matrix‐dominant properties, resulting in superior composites. We investigated the morphological, rheological and mechanical properties of the glass fiber‐reinforced multiscale composites. The results from testing for tensile properties and interlaminar shear strength (ILSS) of the glass fiber‐reinforced composites indicate that the introduction of GNPs, GO, rGO, and MWCNTs enhanced mechanical properties. The fracture surfaces of the fiber‐reinforced composites were examined by scanning electron microscopy and we used the micrographs to explain the tensile and ILSS results. POLYM. COMPOS., 40:E1732–E1745, 2019. © 2018 Society of Plastics Engineers
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it