Structural and Conductive Adhesives Enabled by Single-Walled Carbon Nanotubes
Bibliographic record
Abstract
Adhesives are increasingly being employed as alternatives to mechanical fasteners in aerospace and other engineering applications. While structural adhesives are commonly employed on aircraft, electrical bonding continues to be achieved with rivets as the existing electrically conductive adhesives suffer from low strength due to the high loading (typically 25-30 vol.%) of conductive filler particles. Carbon nanotubes are one of the more attractive candidates for development of multifunctional adhesives for both structural and conductive bonding because of their high conductivity and high aspect ratio. The latter enables creation of conductive pathways at much lower loading (< 1 vol.%) and, therefore, conductivity can be achieved without degrading mechanical performance. Single-walled carbon nanotubes (SWCNTs) offer the highest intrinsic conductivity and aspect ratio as well as the lowest percolation threshold, which is associated with higher conductivity at a fixed loading. In this work, SWCNTs were incorporated at low loading (0.5 - 3 wt%) into an unfilled aerospace-grade epoxy system, to impart electrical conductivity while maintaining structural bonding capability. Mechanical properties of composite-tocomposite joints were evaluated using ASTM-based lap shear and peel tests. Bulk electrical conductivities over 1 S/m were achieved without degrading the joint structural performance in the selected test methods. The mechanical and electrical performance of a SWCNT-modified epoxy adhesive was also studied for aluminumto- 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 slightly lower than the bulk electrical conductivity measured on thicker samples.
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 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) | 0.000 | 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 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".