Quiescent crystallization of polypropylene: Experiments and modeling
Bibliographic record
Abstract
The quiescent crystallization of several polypropylenes (PPs) was examined using Differential Scanning Calorimetry (DSC) and Polarized Optical Microscopy (POM). The half-times of crystallization were obtained from the DSC thermographs employing the Avrami/Nakamura equation to fit and predict crystallization kinetics under isothermal and nonisothermal conditions. The induction times under nonisothermal conditions were estimated from isothermal crystallization data and used in conjunction with the Nakamura model in order to capture the crystallization behavior of the studied PPs. The Avrami/Nakamura model is found to fit and predict the nonisothermal crystallization data of the various PPs well over a range of cooling rates supporting its use in the simulation of polymer processes of industrial relevance. POM was used in line with parallel plate rheometry (Anton Paar, MCR 502) under no flow conditions to study the shape and growth rate of crystals of various PP resins at different temperatures or cooling rates. The growth rate of crystals is impeded exponentially with increase of temperature. The various PP resins of different molecular architecture have shown different nucleation and growth rate characteristics behavior under similar processing conditions. © 2014 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2014, 52, 1259–1275
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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 | 0.001 |
| 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 | 0.001 | 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".