Dispersion Characteristics, the Mechanical, Thermal Stability, and Durability Properties of Epoxy Nanocomposites Reinforced with Carbon Nanotubes, Graphene, or Graphene Oxide
Bibliographic record
Abstract
Carbon-based nanoparticles (CBNs) are regarded as promising nanofillers in nanocomposites to produce high-performance fiber-reinforced polymers (FRPs). To date, no systematic investigations have been carried out on the structural variations of nanofillers and their influences on dispersion characteristics, which give nanocomposites their mechanical and durability properties. Moreover, environmentally unfriendly organic solvents are used to exfoliate and disperse CBNs in a polymer matrix. This study developed a green, easy approach to preparing epoxy/CBN nanocomposites. We demonstrated graphene oxide's (GO) effective dispersion capacity, creating good interface interaction that dramatically influenced properties at loadings as low as 0.4 wt%. The tensile strength and toughness of the epoxy increased by about 49%; and 160%, respectively. Incorporating 0.4 wt% of multi-wall carbon nanotubes (MWCNTs), graphene nanoplates (GNPs), or GO into the epoxy increased the modulus storage by around 17%, 25%, and 31%, respectively. Fractography analysis of fracture surfaces indicated the primary reinforcing mechanisms (crack deflection and penning) as well as the secondary mechanism (bridging effect) enhancing the mechanical characteristics of nanocomposites. Incorporating GNPs, GO, or MWCNTs into the epoxy decreased the water absorption at saturation by about 26%, 22%, and 16%, respectively.
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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) | 0.000 | 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 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".