Electrospun Green Fibers from Alberta Oilsands Asphaltenes
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Alberta oilsands asphaltenes (AOAs) are carbon-rich hydrocarbons obtained from the heaviest fraction in Alberta oilsands bitumen. They have little value in the current market. Asphaltenes are considered a problematic stream for bitumen transportation and processing, and they may be a potential feedstock for carbon fiber (CF) production. Effort has been devoted by researchers and the oil industry for developing asphaltenes into value-added products, in particular CFs. Major barriers have been identified for the conversion of asphaltenes to CFs. One of them is purification and priming of the AOA feedstock as the raw material varied significantly from extraction and applied isolation technologies. Here, we report the purification of raw AOAs for the purpose of forming AOA-green fibers through electrospinning, the comparison with the non-purified AOA raw materials, and the validation of the potential of conversion of asphaltenes toward CFs. Thermogravimetric analysis, elemental analysis, and scanning electron microscopy were carried out. AOA-green fibers were obtained with the as-received AOAs and the maltene-free AOAs by three optimized electrospinning protocols. These green fibers can be spun to a large size mat with a high degree of alignment through adjusting the collector rotation velocity. The diameters of the obtained AOA-green fibers are mostly in the range of 4–15 μm. The green fibers from the as-received AOAs could sustain up to 200 °C in air but fused with further increase of temperature, while the green fibers from the purified AOAs showed improved mechanical strength and were able to withstand temperatures up to 300 °C in air without fusing. This work will be of interest to the CF industry as a potential alternative approach for low-cost precursors.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".