Hemp hurd filled <scp>PLA</scp> ‐ <scp>PBAT</scp> blend biocomposites compatible with additive manufacturing processes: Fabrication, rheology, and material property investigations
Bibliographic record
Abstract
Abstract A co‐continuous 50:50 blend of poly(lactic acid) (PLA) and poly(butylene adipate‐co‐terephthalate) (PBAT) was compounded with 10–40 wt% hemp hurd microparticles (<250 μm) to achieve balanced material properties for additive manufacturing (3D printing) applications. The blend and composites were further compatibilized with maleic anhydride via an in situ reactive extrusion process to enhance the adhesion between the polymers and the fillers and subsequently improve stress transfer between the components. All composite samples were processed further via injection molding and 3D printing processes to analyze and compare the rheology, morphology, thermal, mechanical properties, and other physio‐chemical properties. For the unfilled polymer blends, the injection molded and 3D printed sample specimens displayed similar mechanical properties. However, in the hemp filled composites, the injection molded specimens demonstrated enhanced mechanical properties. This indicates that the 3D printing process requires further parameter optimization to eliminate potential gaps between the printing layers and enhance mechanical properties. Overall, the PLA:PBAT blends filled with hemp hurd microparticles provided balanced composite properties in addition to their sustainability attribute, which makes them an appealing alternative for the fabrication of compostable and tough materials via additive manufacturing processes. Highlights Upon maleation, the blend components formed a co‐continuous morphology. Sustainable plastic filament with up to 40 wt% hemp hurd micro particles. 3D printing can compete with injection molding in filled composites. Successfully 3D printed model furniture with 30 wt% hemp filler.
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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".