Molecular Structure – Processing – Property Relationship in Polypropylene Foams Fabricated using Supercritical CO2
Bibliographic record
Abstract
This thesis investigates the structure-process-property relationship in polypropylene (PP) foams prepared using supercritical carbon dioxide. It aims to bridge the gap in research linking foamability to upstream material properties without needing additional downstream processes, focusing on the low- and high-temperature regimes. Initial studies identified crystallization temperature and gas diffusivity as key factors in PP expansion. Building on this work, the thesis explores the foamability of linear and long-chain branched PP through measurable parameters obtained via fundamental characterization. The study confirms that the onset crystallization temperature (Tc-onset) is crucial at low temperatures, while identifying strain hardening ratio (SHR) as a reliable numerical indicator of high-temperature foaming performance. The research then systematically modifies PP’s molecular structure to tune Tc-onset and SHR, thereby controlling foaming behavior in different temperature regimes. In the low-temperature regime, the introduction of ethylene random comonomers to PP was investigated to tune Tc-onset and enhance foamability. The study showed that while a low Tc-onset is necessary for high expansion at low temperatures, it can delay cell stabilization at high temperatures, compromising its expansion. This finding highlighted the dynamics of the PP expansion profile when systematically varying Tc-onset while maintaining SHR similar. However, it is understood that Tc-onset evaluated under quiescent conditions may be irrelevant in a foaming context. Therefore, this thesis further explores the role of extensional flow-induced crystallization in PP expansion using a unique system. It was found that long-chain branched PP undergoes extensional flow-induced crystallization more intensely and at faster rates, emphasizing its importance in cell stabilization and its complementing relationship with SHR. For high-temperature foaming, ionic modification of PP was shown to present an alternate cost-effective route towards enhancing SHR and, thus, foamability compared to traditional long-chain branching. Finally, through a comprehensive statistical analysis, the research highlighted alternative material parameters that can be adjusted to improve the foamability of PP, while mapping the PP expansion profile according to regions dominated by specific parameters. This approach offers valuable guidelines for industrial product developers to tailor PP resins during development, thereby enhancing foaming performance without additional processing steps.
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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".