Failure analysis of self-healing epoxy resins using microencapsulated 5E2N and carbon nanotubes
Bibliographic record
Abstract
Abstract Investigation on self-healing properties of epoxy containing microcapsules of pure 5-ethylidene-2-norbornene (5E2N) and mixed multi-walled carbon nanotube (MWCNT) suspension is presented in this work using lap shear and mode I fracture tests. While both systems show significant self-healing functionalities between 39% and 97%, those repaired with MWCNT/5E2N microcapsules are found to have mechanical recovery efficiencies two times higher than those containing 5E2N alone, under different types of loading. MWCNT/5E2N microcapsules are also found to improve the physical strength of the epoxy matrix much higher than microcapsules containing only 5E2N. Compared to self-healing epoxy systems made of 5E2N microvessels, the dispersion of 0.10 wt.% of only MWCNTs inside the encapsulated monomer increases the lap shear strengths of the pristine materials and the self-healing materials tested after one fracture/curing cycle, by about 50% and 250%, respectively. Using fractographic observations, Raman spectroscopy and scanning electron microscopy, we observe the occurrence of a ring opening metathesis polymerization reaction and the contribution of MWCNTs to strengthening the material. The present work indicates that the MWCNT/5E2N microcapsule when incorporated into an appropriate epoxy formulation can serve as efficient self-healing matrix material for laminated composites, as well as self-healing structural adhesives for composite bonded joints, two features that make this system highly relevant for applications in aerospace engineering.
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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) | 0.001 | 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".