Plasma-Enhanced Alginate Pre-Treatment of Short Flax Fibers for Improved Thermo-Mechanical Properties of PLA Composites
Bibliographic record
Abstract
This research centered on enhancing the mechanical properties of sustainable composite materials made from short flax fibers. Challenges associated with fiber–matrix adhesion and moisture absorption were systematically addressed. A water–alginate pre-treatment, combined with plasma modification, was employed to stabilize the fibers, ensuring their optimal preparation and improved compatibility with biopolymers. A thorough investigation of the effect of the plasma modulation using a duty cycle (DC) was conducted, and extensive physicochemical and mechanical analyses were performed. These efforts revealed conditions that preserved fiber integrity while significantly improving surface characteristics. Techniques such as optical emission spectroscopy (OES), Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), and Dynamic Mechanical Analysis (DMA) were utilized, providing a comprehensive understanding of the transformations induced by the plasma treatment. The findings underscored the critical role of alginate and precise plasma settings in enhancing the mechanical properties of the composites. Ultimately, this study made a substantial contribution to the field of eco-friendly materials, showcasing the potential of short flax fibers in sustainable composite applications and setting the stage for future advancements in this area.
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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".