Effect of Graphene Oxide as a Reinforcement in a Bio-Epoxy Composite
Bibliographic record
Abstract
Graphene oxide (GO) has gained interest within the materials research community. The presence of functional groups on GO offers exceptional bonding capabilities and improved performance in lightweight polymer composites. A literature review on the tensile and flexural mechanical properties of synthetic epoxy/GO composites was conducted that showed differences from one study to another, which may be attributed to the oxidation level of the prepared GO. Herein, GO was synthesized from oxidation of graphite flakes using the modified Hummers method, while bio-epoxy/GO composites (0.1, 0.2, 0.3 and 0.6 wt.% GO) were prepared using a solution mixing route. The GO was characterized using Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM) and transmission electron microscope (TEM) analysis. The thermal properties of composites were assessed using thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). FTIR results confirmed oxidation of graphite was successful. SEM showed differences in fractured surfaces, which implies that GO modified the bio-epoxy polymer to some extent. Addition of 0.3 wt.% GO filler was determined to be an optimum amount as it enhanced the tensile strength, tensile modulus, flexural strength and flexural modulus by 23, 35, 17 and 31%, respectively, compared to pure bio-epoxy. Improvements in strength were achieved with considerably lower loadings than traditional fillers. Compared to the bio-epoxy, the 0.6 wt.% GO composite had the highest thermal stability and a slightly higher (positive) glass transition temperature (Tg) was increased by 3.5 °C, relative to the pristine bio-epoxy (0 wt.% GO).
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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) | 0.000 | 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 | 0.000 | 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".