Thermal, Mechanical, and Rheological Properties of PLA/PHB Biocomposites Reinforced with Alkaline-Treated Hemp Fibers and Granules
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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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".