A review of recent progress in improving the fracture toughness of epoxy‐based composites using carbonaceous nanofillers
Bibliographic record
Abstract
Abstract Epoxy resins (EPs) exhibit various extraordinary properties, including significant mechanical and thermal properties, low shrinkage, and high chemical resistance, opening a wide window of different applications such as adhesives, paints, coatings, etc. By contrast, EPs also have the undesirable behavior of being brittle and cannot sufficiently resist against the initiation and growth of cracks. Efforts are being made to enhance the toughening of EPs without sacrificing their other desirable properties. With the advent of nanotechnology, improving the toughening of EPs has gained momentum by incorporating different modified and unmodified nanofillers into these polymers. Since the discovery of carbonaceous nanofillers, especially carbon nanotubes (CNTs) and graphene (Gr), significant progress has been made in the development of EP‐based composites incorporating these nanofillers and their hybrids. The current review presents research progress during the last six years on the toughening of EPs using CNTs, Gr, and CNT‐Gr hybrids. Special attention is given to the chemical functionalization of these nanofillers, which has been demonstrated over and over again to significantly affect nanofiller dispersion in the EP matrix and subsequently its fracture properties. Details on the various toughening mechanisms of EP‐based composites are further provided.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".