Performance evaluation of biodegradable polymer <scp>PHBV</scp> and <scp>PBAT</scp> blends with adjustable melt flow behaviour, heat deflection temperature, and morphological transition
Bibliographic record
Abstract
Abstract Melt blending is a reliable and well‐demonstrated strategy for improving the mechanical, thermal, rheological, and surface properties of biopolymers. Poly(hydroxy‐3‐butyrate‐co‐3‐hydroxyvalerate) (PHBV) and poly(butylene adipate‐co‐terephthalate) (PBAT) are the two popular choices for blending polymers due to their diverse properties and complementary soil biodegradable behaviour. Due to their immiscibility, however, blending with the help of processing additives is necessary to reap the most significant benefits from this process and to avoid immiscibility issues. This study utilized the additives (peroxides and epoxy‐based chain extender) to compatibilize the biodegradable polymers PHBV and PBAT in a 60:40 blending ratio. The tensile strength and Young's modulus of the PHBV/PBAT(60/40) blend were improved by 32% and 64%, respectively, after adding a combination of peroxide (0.02 phr) and chain extender (0.3 phr) due to the formation of a complex network structure with increased chain length. The positive effect of an additive addition was also reflected by a 30°C increment in heat deflection temperature of biodegradable blend due to its high modulus value as supported by mechanical properties. The combined action of a peroxide and chain extender demonstrated a significantly higher complex viscosity of the PHBV/PBAT(60/40) blend due to the formation of a crosslinked polymer network as analyzed by rheological analysis. Our research demonstrated the effect of additives and their combined impact on analytical properties of PHBV/PBAT(60/40) blend to guide future work in improving their candidature to serve as a drop‐in solution in replacing non‐biodegradable petro‐based plastic 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) | 0.001 | 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".