Mechanical Properties of a Biocomposite Based on Carbon Nanotube and Graphene Nanoplatelet Reinforced Polymers: Analytical and Numerical Study
Why this work is in the frame
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Bibliographic record
Abstract
Biocomposites based on thermoplastic polymers and natural fibers have recently been used in wind turbine blades, to replace non-biodegradable materials. In addition, carbon nanofillers, including carbon nanotubes (CNTs) and graphene nanoplatelets (GNPs), are being implemented to enhance the mechanical performance of composites. In this work, the Mori–Tanaka approach is used for homogenization of a polymer matrix reinforced by CNT and GNP nanofillers for the first homogenization, and then, for the second homogenization, the effective matrix was used with alfa and E-glass isotropic fibers. The objective is to study the influence of the volume fraction Vf and aspect ratio AR of nanofillers on the elastic properties of the composite. The inclusions are considered in a unidirectional and random orientation by using a computational method by Digimat-MF/FE and analytical approaches by Chamis, Hashin–Rosen and Halpin–Tsai. The results show that CNT- and GNP-reinforced nanocomposites have better performance than those without reinforcement. Additionally, by increasing the volume fraction and aspect ratio of nanofillers, Young’s modulus E increases and Poisson’s ratio ν decreases. In addition, the composites have enhanced mechanical characteristics in the longitudinal orientation for CNT- reinforced polymer and in the transversal orientation for GNP-reinforced polymer.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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