Rheological properties of crystallizing polylactide: Detection of induction time and modeling the evolving structure and properties
Bibliographic record
Abstract
Abstract Linear viscoelastic properties of polymer melts are highly sensitive to any structural changes, including molecular weight changes and the formation and growth of crystallites. Here, we make use of this sensitivity to study the homogeneous crystallization of polylactide. As this polymer is rather quickly susceptible to thermal degradation even at moderate temperatures, it is essentially impossible to study homogeneous crystallization in the absence of degradation. Thus, the evolution of complex viscosity of a polylactide due to thermal degradation in the absence of crystallization was studied. A simple empirical model is used to characterize the variation of complex viscosity due to thermal degradation and to determine the induction time of homogeneous crystallization at wide range of degrees of supercooling. Next, the evolution of complex viscosity due to crystallization was measured at several temperatures. Based on the results, a phenomenological model describing the viscosity evolution during homogeneous crystallization is proposed and validated. Finally, the linear viscoselastic data in the early stages of crystallization are shown to be consistent with gelation due to the formation of a network of tie molecules between spherulites. © 2010 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 48: 812–822, 2010
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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) | 0.000 | 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 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".