Characterization of multiscale glass fibre/unsaturated polyester composites with high graphene concentrations: A comparative study of incorporation techniques
Bibliographic record
Abstract
This study investigates two incorporation techniques for integrating high concentrations (2–6 wt%) of industrial-grade mass-produced graphene into glass fibre-reinforced unsaturated polyester composites to create multiscale composites with enhanced functionality. The incorporation methods compared were direct mixing of graphene into the resin matrix and spray coating of graphene suspension onto fibre surfaces, along with a hybrid approach combining both techniques. A fabrication methodology for the multiscale composites was developed to address processing challenges associated with high graphene concentrations and increased resin viscosity. Mechanical characterization revealed that flexural strength and modulus decreased by up to 15 % and 9 %, respectively, with fibre-coated composites showing greater deterioration than resin-mixed samples. In contrast, interlaminar shear strength (ILSS) increased by up to 12 % when graphene was directly mixed into the resin, due to toughening of resin-rich mid-plane in the laminate structure. Fibre coating, however, resulted in reduced mechanical properties due to impaired resin infiltration and increased void content, as confirmed by optical microscopy and permeability measurements, which showed a decrease from 1.3 × 10⁻¹¹ m² for the neat preform stack to 3.8 × 10⁻¹² m² for the fibres coated with 4 wt% graphene. Electrical conductivity analysis demonstrated that graphene-coated fibre multiscale composites achieved the lowest percolation threshold at 2.7 wt%, compared to 3.1 wt% for resin-modified composites, attributed to preferential localisation of graphene along fibre surfaces. These findings provide important insights into the relationship between graphene incorporation technique, resulting microstructure, and composite properties in high-concentration multiscale systems.
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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) | 0.001 | 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 | 0.000 | 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".