Carbon Nanotube Based Network Heaters for Composite Adhesive Bonding
Bibliographic record
Abstract
Adhesive bonding and bonded repair of metallic and composite aircraft structures have been used as effective methods for manufacturing and for restoring structural integrity of aircraft structures. Adhesive bonding requires the application of heat in order to cure the adhesive and complete the bonding operation; however, conventional heating methods are subject to several drawbacks and might be undesirable, particularly in cases involving repair of new, exotic aerospace materials. The motivation of this work is to develop a novel, cost-effective bonding method and apparatus using a network of carbon nanotubes (CNTs) to provide heating with uniform bondline temperature, rapid temperature response, and minimal energy cost by producing heat directly at the bondline. Such a solution is achieved through integrating a paper-like CNT network (i.e., buckypaper sheet) within film adhesive. This self-heated adhesive layer is applied in the same was as conventional film adhesive and cured through application of a voltage (or current) across the CNT network to cause Joule heating. The approach gives excellent temperature uniformity, fast response, and low energy consumption among other advantages.
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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 | 0.000 |
| 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) | 0.003 | 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".