A Comparison of the Rheological and Mechanical Properties of Isotactic, Syndiotactic, and Heterotactic Poly(lactide)
Bibliographic record
Abstract
A series of poly(lactide) (PLA) samples, exhibiting various levels of syndiotactic enrichment, were formed via the ring-opening polymerization of meso -lactide using two families of dinuclear indium catalysts: ( RR / RR )-[(NNO)InCl] 2 (μ-Cl)(μ-OEt) ( 1 ) and ( RR / RR )-[(ONNO)In(μ-OEt)] 2 ( 2 ). Isotactic and heterotactic PLAs were also synthesized using known methodologies, and the thermal and rheological behaviors of these PLAs with different microstructures were compared. Solution rheological studies showed that the values of intrinsic viscosities and hydrodynamic radii as functions of molecular weight ( M w ) were highest for iso-PLAs, followed by hetero and then syndio-PLAs. The viscosities of the heterotactically enriched PLAs were in agreement with literature values reported for atactic PLAs. The molecular weight between entanglements ( M e ) was greatest for the syndiotactically enriched PLAs, giving rise to the lowest zero-shear viscosity. In addition, hetero- and isotactically enriched PLA had higher flow activation energies ( E a,flow ) than syndiotactic variants, implying the inclusion of transient aggregate regions within these polymers due to enhanced L- and D-interactions. Although strain hardening was observed for all types of PLAs, it was more dominant for isotactic PLAs due to stronger L- and D-interactions possibly leading to a small degree of stereocomplex microcrystallites.
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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".