Exploring The Rheological, Crystallization, And Mechanical Property Behaviour Of Stereocomplexed Poly(Lactide) Blends, And Their Crystalline Network Structures
Bibliographic record
Abstract
Poly(L-lactide) (PLLA) is a bio-based and compostable polymer with significant potential for replacing petroleum-derived polymers. However, its poor crystallization kinetics, melt strength and low thermal resistance limit PLLA's processability and application in high-performance components. Nevertheless, these issues can simultaneously be overcome by melt-blending PLLA with poly(D-lactide) (PDLA) to form stereocomplex crystallites with a melting temperature about 50C above that of neat homopolymers. These stereocomplex crystallites have a strong nucleating effect and remarkably enhance PLLA's melt strength, while imparting enhanced thermal resistance. Moreover, if drawn from the melt-state, the stereocomplex crystallites can be transformed into in situ nanofibers. The incorporation of stereocomplex nanofibers in PLLA has been linked to noticeable improvements to crystallization behaviour and a reduction in boiling water shrinkage, relative to conventional spherulitic stereocomplex crystallites. Synthesizing from previous work, the aim of this current work is to tune the properties of in situ fibrillated PLLA/PDLA blends to demonstrate the applicability of PLA as a high-performance polymer. This is done through studying the in situ fibrillation of PLLA blends containing varying concentrations of PDLA, with comparison to neat PLLA and non-stretched blend analogues. A comprehensive comparison between stretched and non-stretched blends reveals the development of concentration-dependent network structures of polymorphic nature. Below the critical concentration for network formation, differences between the properties of stretched and non-stretched blends differ greatly. Above this concentration, blend properties are rather similar. The effect of blend composition, multi-tiered morphology, and polycrystallinity, are systematically investigated to identify structure-property relationships.
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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 | 0.001 | 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".