Crystallization and performance evolution of PHBV nanocomposites through annealing: The role of surface modification of CNCs
Bibliographic record
Abstract
To address the brittleness challenge of polyhydroxybutyrate-co-valerate (PHBV) rooting from its slow nucleation rate, cellulose nanocrystals (CNCs) were employed as bio-based nano nucleation and reinforcement agents. CNCs were further functionalized through succinylation using aliphatic dodecyl succinic anhydride (DDSA), for improved compatibility and dispersion within PHBV. Nanocomposites of PHBV incorporating pristine or DDSA-modified CNC (mCNC) were prepared through solution mixing followed by melt injection molding. The study focused on investigating how CNCs and mCNCs affect the crystallization behavior, thermal, rheological, and mechanical properties of PHBV nanocomposites over time, in the course of annealing or conditioning. Thermal results revealed that the incorporation of neat CNCs generally improving stability due to restricted polymer chain mobility and hydrogen bonding, while DDSA-modified CNCs show varied effects depending on the concentration, sometimes diminishing stability due to increased chain mobility. Polarized optical microscopy revealed the superior nucleation efficiency of mCNC, especially at low contents, leading to smaller and numerous spherulites over conditioning. Rheological analysis indicated a dilution effect of the hairy mCNCs, decreasing both dynamic modulus and complex viscosity. Mechanical properties, assessed through tensile testing and dynamic mechanical analysis after 15 days of conditioning, demonstrated the evolving effect of CNCs on aging-induced embrittlement and thermo-mechanical performance of PHBV during storage. The results revealed that incorporating 1 wt% mCNCs effectively toughened PHBV, increasing Young's modulus, and decreasing Tg without scarifying elongation at break compared to neat PHBV. The findings position mCNCs as a promising nucleation agent which retains PHBV nanocomposites toughness after aging.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".