Material Extrusion of <scp>PLA</scp> /Recycled Short Flax Fiber Biocomposites
Bibliographic record
Abstract
ABSTRACT Biocomposites are sustainable alternatives to conventional composites due to their biodegradability and renewability. This study investigates the mechanical properties of 3D‐printed flax fiber‐reinforced polylactic acid (PLA) composites with fiber weight fractions ranging from 0% to 5%, and examines the effects of a 2% (w/v) sodium hydroxide solution treatment on the composite properties. Adding untreated fiber reduces the mechanical properties of single filaments, while the addition of treated fiber improves tensile strength by 16% and tensile modulus by 14% with up to 5 wt.% flax fiber content in the biocomposite. The treated fibers significantly increase the stiffness of the 3D‐printed samples, with an 81% rise in Young's modulus. Compared to pure PLA samples, a 41% improvement is observed with 2.5 wt.% content of treated flax fiber. However, tensile strength decreases by 35% across all samples, with greater reductions following treatment. Compression tests show a 23% strength increase for samples with untreated fibers, but performance declines with chemical treatment. Microscopic images reveal voids that impact mechanical properties, while Fourier‐transform infrared spectroscopy confirms the effects of fiber surface modification. These findings highlight the trade‐offs between improving stiffness and reducing strength when pre‐treatment of flax fibers are used, and underscore the potential of flax fiber‐reinforced PLA biocomposites in sustainable additive manufacturing for environmentally‐benign a advanced material production.
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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".