A Comparative Study on Mechanical and Electrical Properties of SWCNT-Modified Epoxy Resins and Their End-Use Applications
Bibliographic record
Abstract
In this work, the influence of SWCNTs on electrical and mechanical performance of two epoxy resins was studied. In the first study, a small quantity of SWCNTs (0.125 wt%) was incorporated into a high-temperature aerospace-grade epoxy. After the modification with SWCNTs, the correlation between bulk properties of this epoxy (electrical conductivity and Mode I fracture toughness) and the properties of epoxy/glass fiber laminates (inplane/ transverse electrical conductivity and Mode I interlaminar fracture toughness) was investigated. It was found that the electrical property improvements of nano-modified laminates exceeded those of bulk SWCNT-modified epoxy. On the other hand, the fracture toughness improvement of bulk epoxy after the addition of SWCNTs did not translate into interlaminar properties enhancement, possibly due to reduction of the nano-modified resin's ability to interact with glass fiber mats. In the second study, the mechanical and electrical performance of a SWCNT-modified epoxy adhesive was studied for aluminum-to-aluminum bonding. It was found that the integration of 1 wt% SWCNTs can considerably improve joint Mode I fracture toughness by ~ 35% due to mechanisms such as crack bridging. The electrical resistance of the bondline was also consistent with the electrical conductivity of SWCNT-modified adhesive films (~10-3 S/m), but somewhat lower than the bulk electrical conductivity measured on thicker samples. © 2012 by Canada National Research Council.
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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) | 0.000 | 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) | 0.001 | 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".