Mechanical Properties of a Biocomposite Based on Carbon Nanotube and Graphene Nanoplatelet Reinforced Polymers: Analytical and Numerical Study
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.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".