Study on the 3D printability of poly(3-hydroxybutyrate-co-3-hydroxyvalerate)/poly(lactic acid) blends with chain extender using fused filament fabrication
Bibliographic record
Abstract
Abstract In this study, the 3D printability of a series of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV)/poly(lactic acid) (PLA) blends were investigated using fused filament fabrication (FFF). The studied blends suffered from poor 3D printability due to differences in compatibility and low thermal resistance. These shortcomings were addressed by incorporating a functionalized styrene-acrylate copolymer with oxirane moieties as a chain extender (CE). To enhance mechanical properties, the synergistic effect of 3D printing parameters such as printing temperature and speed, layer thickness and bed temperature were explored. Rheological analysis showed improvement in the 3D printability of PHBV:PLA:CE blend by allowing a higher printing temperature (220 °C) and sufficient printing speed (45 mm s−1). The surface morphology of fractured tensile specimens showed good bonding between layers when a bed temperature of 60 °C was used and a layer thickness of 0.25 mm was designed. The optimized printing samples shown higher storage modulus and strength, resulting in stiffer and stronger parts. The crystallinity, morphology and performance of the 3D printed products were correlated to share key methods to improve the 3D printability of PHBV:PLA based blends which may be implemented in other biopolymer blends, and further highlight how process parameters enhance the mechanical performance of 3D printed products.
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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) | 0.000 | 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".