Effect of Graphene Additive on Flexural and Interlaminar Shear Strength Properties of Carbon Fiber-Reinforced Polymer Composite
Bibliographic record
Abstract
Composite materials are widely used in various manufacturing fields from aeronautic and aerospace industries to the automotive industry. This is due to their outstanding mechanical properties with respect to their light weight. However, some studies showed that the major flaws of these materials are located at the fiber/matrix interface. Therefore, enhancing matrix adhesion properties could significantly improve the overall material characteristics. This study aims to analyze the effect of graphene particles on the adhesion properties of carbon fiber-reinforced polymer (CFRP) through interlaminar shear strength (ILSS) and flexural testing. Seven modified epoxy resins were prepared with different graphene contents. The CFRP laminates were next manufactured using a method that guarantees a repeatable and consistent fiber volume fraction with a low porosity level. Short beam shear and flexural tests were performed to compare the effect of graphene on the mechanical properties of the different laminates. It was found that 0.25 wt.% of graphene filler enhanced the flexural strength by 5%, whilst the higher concentrations (2 and 3 wt.%) decreased the flexural strength by about 7%. Regarding the ILSS, samples with low concentrations (0.25 and 0.5 wt.%) demonstrated a decent increase. Meanwhile, 3 wt.% slightly decreases the ILSS.
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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".