Crystallization Kinetics of Linear and Long-Chain-Branched Polylactide
Bibliographic record
Abstract
In this study, the non-isothermal cold crystallization and isothermal melt crystallization of both linear and long-chain-branched (LCB) polylactide (PLA) were investigated using a differential scanning calorimeter (DSC). Talc was used as a nucleating agent to promote crystallization. The effects of chain branching on PLA’s cold crystallization kinetics at different heating rates and on PLA’s melt crystallization kinetics under different temperatures were studied by using Avrami analysis. The results showed that LCB-PLAs have faster cold and melt crystallization rates than those of linear PLA, since branched chains can play a role of nucleating site. Talc is a powerful nucleating agent, especially for linear PLA, either in cold crystallization or melt crystallization process. It was seen that addition of talc to PLA improves the crystallinity of PLA samples with more linear structure, more effectively because of its role of crystal nucleation. In PLA samples with more branched structure, talc has the least effect on crystallinity suggesting that the branched structure dominated crystallization already regardless of the presence of talc. Isothermal melt crystallization experimental results also showed that branched PLAs crystallized much faster than linear PLA and talc could increase the melt crystallization rate of linear PLA, but not that of PLA with a more branched structure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| 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".