Electrospinning Asphaltenes With Low-Cost Polymers: Towards High-Value Carbon Materials
Bibliographic record
Abstract
As a large exporter of oil, Canada produces millions of barrels each day. The heaviest component of crude oil contains a class of molecules called asphaltenes. Asphaltenes are polyaromatic hydrocarbons (PAHs) with a varying degree of alkyl chains and heteroatoms. These molecules are known to cause several issues with oil production and transportation, as they tend to aggregate and adsorb onto surfaces. This results in reduced oil production, the need for diluents, and increased cost to clean and replace equipment. While asphaltenes are used in asphalt, they represent a massive waste byproduct that if removed at the source could clean our oil sector and be used as building blocks for new carbon-based materials and technology. To this end, this research focuses on the valorization of asphaltenes en route to high-value carbon nanomaterials. A technique known as electrospinning is used to create asphaltene-containing nanofibers that could then be pyrolyzed to produce carbon-based fibers. Unfortunately, p-stacking is a challenge with electrospinning asphaltene and therefore low-cost polymer additives (e.g. lignin) have been explored to disrupt aggregation and improve the spinnability of asphaltene. The combination of asphaltene/polyvinylalcohol/lignin (MassRatio) proved most successful, and the attempt leading to this composition and the resulting properties of the nanofibers is discussed herein.
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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 | 0.001 |
| 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".