Enhancing Asphaltene Spinnability via Polymer Blending
Bibliographic record
Abstract
The drive toward carbon neutrality has led to extensive research on converting asphaltenes (AS) into value-added products. AS are composed of large polyaromatic and polyaliphatic cyclic systems with a low H-to-C ratio, making them well-suited to produce carbonaceous materials, such as carbon fibers. However, the inherent variability in the viscoelastic properties of AS, which stems from their sourcing, poses challenges to the spinnability of AS-derived carbon fibers. In this study, we intentionally chose an AS sample that inherently lacks the ability to be spun continuously and enhanced its viscoelasticity by incorporating thermoplastic polymers with comparable Hansen solubility parameters. Three weight fractions of polystyrene (PS), poly(methyl methacrylate), and poly(ethylene- co -vinyl acetate) (EVA) (5, 10, and 20%) were added to pristine AS powder by two common mixing methods: solution mixing and melt blending. All three blends of AS and polymers exhibited enhanced spinnability compared to pure AS, with the EVA blend showcasing the highest efficacy in the melt-spinning process, producing fibers with the smallest diameter and the least amount of surface defects. Furthermore, we established a correlation among the microstructure of the blends, their rheological properties, and their spinnability. Confocal imaging confirmed enhanced compatibility between AS and EVA, with the blend exhibiting a lower rheological signature compared with its individual components. This can be attributed to the potential of EVA to break down larger AS aggregates, facilitating improved alignment during high-temperature spinning.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".