Thermal, Mechanical, and Rheological Properties of PLA/PHB Biocomposites Reinforced with Alkaline-Treated Hemp Fibers and Granules
Why this work is in the frame
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Bibliographic record
Abstract
This study reports the development of fully biodegradable biocomposites based on polylactic acid (PLA) and polyhydroxybutyrate (PHB) reinforced with alkaline-treated hemp fibers and granules. The thermal, mechanical, dynamic mechanical, and rheological properties of the composites were investigated to assess the influence of reinforcement morphology and content. Differential scanning calorimetry (DSC) confirmed that hemp fibers acted as more effective nucleating agents than granules, increasing the degree of crystallinity of the PLA/PHB blend. Thermal conductivity analysis revealed that hemp incorporation does not systematically improve heat transfer: while long fibers slightly enhanced conductivity, several granule-based composites exhibited lower values than the neat blend. Tensile testing showed that all reinforced samples had lower tensile strength than the neat PLA/PHB matrix, although stiffness was increased, particularly for fiber-based composites. In contrast, flexural strength was maximized in granule-reinforced systems, notably PLA/PHB-2–10-G and PLA/PHB-0.5–10-G, while fibers preserved or improved ductility. Dynamic mechanical analysis confirmed the viscoelastic nature of all composites, with reduced tan δ compared to the neat blend. Rheological testing demonstrated that most composites exhibited lower G′ and complex viscosity than the neat PLA/PHB blend, except for PLA/PHB-2–10-G, which showed stronger matrix–filler interactions. Overall, the results highlight that the performance of PLA/PHB/hemp biocomposites is formulation-dependent, and the selection of hemp morphology and content is crucial for tailoring properties to specific applications.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it