Tailoring Multifunctional and Lightweight Hierarchical Hybrid Graphene Nanoplatelet and Glass Fiber Composites
Bibliographic record
Abstract
In this work, hybrid polypropylene (PP)-based composites reinforced with graphene nanoplatelets (GnPs) and glass fiber (GF) were fabricated by injection molding to elucidate how the hybrid approach can produce synergistic effects capable of achieving properties and functionalities not possible in biphasic composites. Synergism between the reinforcements translated to improved mechanical performance, which was attributed to the chemically and/or electrostatically assembled hierarchical structure that facilitates load transfer at the interface while simultaneously tailoring the crystalline microstructure of the matrix by inducing transcrystallization and β-crystal formation. It was demonstrated that there exists an optimal concentration of 0.5 wt % GnP, producing the greatest mechanical properties and synergistic effect, corresponding to the highest degree of crystallinity (∼6% greater than Neat PP) and peak formation of β-crystals within the PP matrix. The greatest synergistic effect was found to be ∼52 and ∼39% for the specific tensile strength and flexural strength, respectively. The same optimal concentration of GnPs was found to produce the highest synergistic effect for thermal conductivity of ∼68% due to the volume exclusion effect induced by the GFs combined with the higher crystallinity of the microstructure, promoting the formation of thermally conductive pathways. Ultimately, the mechanisms contributing to the synergistic effect presented in this work can be used to maximize the performance of hybrid composite systems, giving them the potential to be tailored for a variety of high-performance industrial applications to meet the rising demands for ultra-strong, thermally conductive, and lightweight materials.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".