Mechanical response of bio-epoxy resin composites reinforced with graphene oxide: machine learning approach for property prediction
Bibliographic record
Abstract
Abstract Polymer matrix composites have become one of the most developed materials due to the possibility of tailored mechanical, physical, or thermal properties. However, important environmental concerns have arisen within conventional thermoset polymers due to the depletion of the non-renewable resources used in their production. For this reason, bio-epoxy resins have been developed by replacing a fraction of the petroleum-based components with renewable materials. In addition, machine learning algorithms have become a powerful tool to estimate material properties in nanocomposites, which might be further corroborated by experimental means. Therefore, this study developed a composite material with a bio-epoxy resin matrix reinforced with graphene oxide (GO). Experimental and theoretical densities revealed a linear relationship between the GO loading and the pore volume fraction of the composites. An estimation of various mechanical properties is listed for a bio-epoxy resin: ultimate tensile strength (UTS) (63.4 MPa), modulus of elasticity (2.74 GPa), flexural strength (95.32 MPa), and flexural modulus of elasticity (2.52 GPa). Composites including 0.1, 0.3, and 0.5 wt. % showed an improvement in the aforementioned properties, while the composites, including 0.8 and 1.2 wt. % exhibit a decrease in the overall mechanical response. Evaluation of tensile and flexural fracture surfaces revealed a noticeable strengthening mechanism in the composites after the addition of the reinforcement. Differential scanning calorimetry results demonstrated increased glass transition temperature as the GO filler loading increased. Machine learning allows the prediction of the UTS as a function of GO content with high accuracy.
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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 | 0.000 | 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) | 0.000 | 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".