In Situ Processing to Achieve High-Performance Epoxy Nanocomposites with Low Graphene Oxide Loading
Bibliographic record
Abstract
Modifying the polymer matrix by nanoparticles can be a promising approach to improve the performance of fiber-reinforced polymer (FRP) composites. Organic solvents are usually used for dispersing graphene oxide (GO) well in the polymer matrix. In this study, a green, facile, and efficient approach was developed to prepare epoxy/GO nanocomposites. In situ polymerization is used for synthesizing nanocomposites, eliminating the need for organic solvents and surfactants. By loading just 0.6 wt% of GO into the epoxy resin, Young’s modulus, tensile strength, and toughness improved by 38%, 46%, and 143%, respectively. Fractography analysis indicates smooth fracture surfaces of pure resin that changed to highly toughened fracture surfaces in this nanocomposite. Plastic deformation, crack pinning, and deflection contributed to improving the toughness of the nanocomposites. FTIR investigations show that amide bonding was created by the reaction of the carboxylic acid groups in GO with some amine groups in the curing agent during the dispersion processes.
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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 | 0.001 |
| 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".