Towards the Next Generation of Lightweight Polypropylene Composites with Well-balanced Mechanical Properties
Bibliographic record
Abstract
Reinforced polypropylene (PP) composites currently find their place in various structural components by integrating the benefits of PP as a matrix and rigid reinforcement filler, largely replacing traditional metals and their alloys. Their low resistance to fracture, however, is often the major limiting factor for applications where catastrophic failure of components must be avoided. Rubber toughening is a common method that addresses this challenge but a trade-off between rigidity and toughness is usually inevitable. To this end, this dissertation aims to uncover the limits of current systems and to explore alternate strategies.The first part of the thesis focuses on optimization of existing polymer formulations for lightweight PP composites via foam injection molding process. The effect of several critical parameters including processing conditions, rubber modifier and reinforcement filler type and loading on their mechanical properties are scrutinized as a function of relative density. The results indicate that, regardless of the relative density or the filler/rubber type, increasing the filler loading leads to gradual enhancement of rigidity and reduction of toughness, while incorporation of rubber modifier displays the reverse trend. Moreover, albeit an effective modifier for toughening of PP composites in general, rubber significantly loses their efficiency for PP composite foams in the presence of nucleated cells as the relative density is reduced further. The second part of the thesis introduces novel techniques for fabrication of lightweight PP composites toughened with in situ generated organic nanofibrils. The first methodology benefits from the formation of core-sheath nanofibrils that are thermodynamically favourable, while the second study combines in situ fibrillation and vulcanization processes to generate partially crosslinked rubber nanofibres. The distinct morphology of both nanofibrillar PP composite systems imparts mechanical properties that are superior to classical PP blends produced with the equivalent material composition. In summary, the outlined research serves as a guideline for the formulation of lightweight PP composites and opens up a new avenue for nanofibre-reinforced PP composite production, broadening the spectrum of their potential applications where excellent strength/toughness balance is indispensable. Furthermore, it provides a detailed framework for understanding the process-structure-property relationships of conventional and nanofibrillar PP composite systems.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".