Experimental Investigation of the Effect of Short Flax Fibers on the Permeability Behavior of a New Unidirectional Flax/Paper Composite
Bibliographic record
Abstract
A new type of reinforcement for unidirectional natural fiber composites has been developed, where a paper layer is assembled with a layer of unidirectional flax yarns. The paper layer chemically and mechanically bonds to the loose yarns to maintain their alignment and enables better manipulability of the reinforcement during stacking in the mold. Unfortunately, the paper layer adversely affects the permeability of the whole reinforcement to liquid resin and thus limits the impregnation quality of the final part. In this paper, a technique is adopted to increase the impregnation performance by modifying the architecture of the fibrous network in the paper layer. In particular, a method has been developed to replace a proportion of the Kraft fibers by short flax fibers in the paper layer, in an attempt to open the structure and increase the paper permeability. Permeability measurements show a major improvement in global reinforcement permeability. Basic mechanical properties of resulting composites were also analysed. Results show a slight decrease in modulus and strength when the paper layer is present. This is compensated by an important reduction in variability. Furthermore, increasing the flax proportion in the paper layer limits the loss of mechanical properties, while reducing variability even further.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".