Synergistic effect of functionalized graphene oxide and carbon nanotube hybrids on mechanical properties of epoxy composites
Bibliographic record
Abstract
esterification reaction and 3D structure hybrids were prepared by combining 1D carbon nanotube (CNT) and 2D functionalized GO through π-stacking interaction. Epoxy composites filled with 3D structure hybrids were fabricated. The results show that functionalized GO effectively improves the dispersibility of CNTs in epoxy matrix due to good compatibility. Excellent mechanical properties were achieved by epoxy composites filled with 3D structure hybrids. The fracture surface analysis indicated improved interfacial interaction between 3D structure hybrids and epoxy matrix, which may due to the covalent bonding formed between the epoxy molecular chain grafted on EGO and the hardener agent during the curing process. In the 3D structure filler network, the mechanisms of crack deflection/bifurcation induced by functionalized GO make the crack path tortuous, which causes the cracks to encounter more CNTs and then promote the mechanisms of CNT fracture and crack bridging, resulting in more energy dissipation. This is the key mechanism for its excellent reinforcing and toughening effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".