Tensile properties improvement of a unidirectional-mat flax/epoxy composite via click chemistry, nanocellulose addition and surface refining of short mat fibres
Bibliographic record
Abstract
The mechanical characteristics of natural fibre composites (NFCs) are closely linked to fibre-matrix and fibre-fibre interactions. This work investigates the improvement of tensile properties of a flax/epoxy composite through the application of click chemistry reaction to a unidirectional-mat (UDM) reinforcement with modifications made on the short fibre mat and unidirectional flax fibre phases of the reinforcement. The surface of short flax fibres was fibrillated to increase the accessibility of hydroxyl groups for all preliminary reactions and the final click chemistry cross-linking, which creates stable covalent triazole bonds between azide and alkyne groups. A small percentage of treated nanocellulose was incorporated to further enhance the reinforcement properties. FTIR and EDX analysis confirmed the presence of the various functional groups on the surface of nanocellulose and flax fibres with very high degrees of substitution. The treatment significantly improved the mechanical properties of the dry reinforcement, including a 220% mean increase in the tensile strength. However, the treatments, particularly the addition of nanocellulose, resulted in a reduction in the permeability to liquid resin of the reinforcements, highlighting the need for compromises in their manufacture. Nonetheless, marked improvements in tensile strength and Young's modulus were obtained for composites made of pre-compacted and cross-linked fibre preforms. Increases in elastic modulus, strength and strain at break of up to 50.1%, 53.8% and 10.1% were obtained, respectively.
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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) | 0.001 | 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".