Exploring the Rheological, Crystallization, and Tensile Properties of Poly(lactide) Stereocomplexes and their Crystalline Network Structures
Bibliographic record
Abstract
Despite numerous benefits, commercialization of poly(L-lactide) (PLLA) is limited due to its low melt-viscosity and poor crystallization kinetics. This work demonstrates that the production of self-reinforced PLLA by a cost-effective, single-step Spunbond process, can overcome these limitations. Herein, poly(D-lactide) (PDLA) and PLLA were melt-blended to form stereocomplex (SC) crystallites that were drawn using blown air. Morphological observations illustrated that upon stretching, the SC crystallites were no longer visible owing to their high compatibility with the matrix but were detectable by calorimetry. Furthermore, rheological analysis revealed the polymorphic nature of network structures in blends after stretching. Interestingly, blend crystallization kinetics and rheological response were greatly enhanced at low PDLA concentrations, after the application of extensional flow. Slight improvements in tensile modulus were also achieved upon stretching. Hence, the use of a strong extensional flow during cooling effectively reduces the amount of PDLA needed to enhance PLLA’s bulk properties, without compromising PLA’s biodegradability.
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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".