Asphaltene/polymer composites: Morphology, compatibility, and rheological properties
Bibliographic record
Abstract
Abstract In this study, we investigate the use of asphaltene, a natural waste product that is inevitably formed during heavy oil processing, as a filler in polymer composites. The focus of this work is on the compatibility of various asphaltenes, featuring different polarities, with several polymers, including polypropylene (PP), polystyrene (PS), polymethyl methacrylate (PMMA), and polycarbonate (PC). The Hansen solubility parameters were plotted to predict the compatibility of polymers with different asphaltenes. Then, polymer composites were prepared by two common techniques: melt mixing and solution mixing. The dispersion state of the asphaltenes in each polymer was investigated by using imaging and rheological techniques. This work showed that the network structure of the asphaltenes and, thus, the final properties of the composites can be controlled by the polarity of asphaltenes, mixing technique, and melt viscosity of the polymer. For instance, more polar asphaltene (Asph P) produced smaller aggregates in PMMA, which has a higher polar solubility parameter than PP or PS. At 2.5 wt.% of asphaltene, Asph P showed 26% and 177% larger asphaltene agglomerates in PP and PS, respectively, than the less polar asphaltene (Asph Al). PS/asphaltene and PMMA/asphaltene composites prepared by the solution mixing method exhibited better dispersion compared to their melt‐mixed counterparts. In melt‐mixed composites, the dispersion quality of the asphaltene was better for polymers with higher melt viscosity. Thus, a careful choice of polymer, asphaltene, and preparation conditions can be used to tune the properties of asphaltene/polymer composites.
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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".