Functionalized interleaf technology in carbon-fibre-reinforced composites for aircraft applications
Bibliographic record
Abstract
At the recent 19th International Conference of Composite Materials (ICCM19), in Montreal, Professor Xiaosu Yi from the Beijing Institute of Aeronautical Materials, Aviation Industry Corporation of China, gave a plenary lecture on ‘How to Make the Structural Composites Multi-functional’. His lecture highlighted the recent developments from his research team in functionalized interleaf technology (FIT). Their work has improved both the electrical conductivity and the impact damage resistance of carbon-fibre-reinforced composites for aircraft applications. Carbon-fibre-reinforced polymer (CFRP) and glass-fibre-reinforced polymer (GFRP) composite structures are widely used in today’s aerospace, green energy, marine, sport and transportation industries. These materials provide manufacturers and builders with costcompetitive alternatives to conventional metal alloys. However, the introduction of polymer composites in mainframes of modern structures presents special challenges and issues regarding their multi-functional properties (e.g. electrical and thermal conductivities) in addition to the potential risk of incurring extension of interlaminar damage under impact and fatigue loading, due to the brittle nature of the matrix resins. For example, such composite structures are poor conductors of extreme electrical currents generated by a lightning strike. Composite materials are either not electrically conductive at all under a moisture-free condition (e.g. GFRPs with electrical conductivity in the order Figure 1. AgNW network; and improvements in interlaminar fracture toughness and electrical conductivity of carbon-fibre-reinforced composites.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".