Development of sustainable polymer composite with agro‐industrial residue for biomedical applications
Bibliographic record
Abstract
Abstract The excessive use of plastics has raised significant environmental concerns, including harm to marine ecosystems and pollution. PLA, a widely used bioplastic, suffers from low toughness and thermal stability, limiting its industrial applications. This study addresses these limitations by incorporating soybean hulls, a biodegradable agricultural waste, as a filler in PLA/PBAT blends to develop sustainable composites for 3D printing. Advanced optimization techniques, including grey relational analysis (GRA) and Taguchi design of experiments (TGRA), were used to optimize printing parameters. Results showed that the raster angle had the most significant influence on mechanical properties (70%). Validation tests using optimized parameters demonstrated a 23% decrease in tensile strength with 10 wt% soybean hulls but a 35% increase in flexural strength and only a 3% reduction in impact strength. These properties make the composite suitable for biomedical and rigid packaging applications. Scanning electron microscopy revealed voids, pullouts, and reduced interlayer adhesion, providing insights into the material's microstructure. This study highlights the innovative use of agricultural waste in 3D printing, combining eco‐friendly composites with advanced optimization techniques to improve sustainability and mechanical performance. Highlights Biodegradable PLA/PBAT composites with soybean hull made via FDM were studied. Raster angle influences 70% of mechanical property variations in composites. Adding 10% soybean hull increased flexural strength by 35% and reduced tensile by 23%. Grey relational analysis optimized printing improves composite properties. Eco‐friendly composites could replace petroleum plastics for biomedical purposes.
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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".