Effects of Wet and Dry Treatments on Surface Functional Groups and Mechanical Properties of Flax Fiber Composites
Bibliographic record
Abstract
Flax fibers have found widespread use in eco-composite materials because of their remarkable mechanical properties compared to glass fibers. However, their low stability limits their use on a larger scale when employed in hot or humid environments. Therefore, the surfaces should be modified before the composite process to provide the best interfacial interactions and increase the dispersion of natural fibers. To tackle this problem, two kinds of modifications can be considered: wet and dry modifications. This research explores different methods to improve the adhesion between flax fibers and the poly lactic acid (PLA) polymer. Morphological and chemical modifications in the presence of acetone, alkali (as a wet modification), and with air atmospheric pressure plasma (as a dry modification) are compared in this research. The results revealed that altering the chemical characteristics on the surface significantly changed the mechanical properties of the final composite. More specifically, the Fourier transform infrared spectroscopy (FTIR) data indicate that wax-related peaks (2850 and 2920 cm−1) were eliminated by both wet and dry treatments. Dynamic mechanical analysis (DMA) results also highlighted that a better bond between the flax fibers and the PLA matrix is obtained with the plasma modification.
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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".