Self-healing of structural carbon fibres in polymer composites
Bibliographic record
Abstract
Carbon fibre–reinforced composites (CFRCs) are increasingly used in aeroplanes, satellites and offshore wind turbines. Access those systems for repair when the material is damaged may be difficult. Researchers have incorporated vascular systems containing healing agents into CFRCs, enabling them automatically to recover from delamination and debonding. However, self-recovery of the structural fibres that give CFRCs their exceptional mechanical properties is still impossible. This paper describes a method to make CFRCs self-heal following structural fibres’ damage. This involves automatically delivering epoxy-based healing agents containing short carbon fibres (SCFs) to cracks through an embedded vascular system. Cracks are created by disk-cutting through the carbon fibre layer of CFRC specimens. The SCFs in the released healing agents can be aligned in a local electric field produced by applying a potential to the broken structural carbon fibres. The alignment reconnects the structural carbon fibres. Process parameters were investigated to observe their effects on the healing performance and determine the optimum healing agent composition and conditions. In comparison to using conventional healing agents without SCFs and electric alignment which restored 25.2% of a CFRC’s original strength, employing the proposed approach increased the recovery to 47.3%.
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 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) | 0.001 | 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".