Functionalized interleaf technology in carbon-fibre-reinforced composites for aircraft applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it