Electrospinning of high-asphaltene content carbon nanofibers and their application as supercapacitor electrodes
Bibliographic record
Abstract
Carbon nanofibers (CNFs) have gained significant attention for applications in composites, supercapacitors, and catalysis, but their commercial viability is limited by the high cost of traditional precursors like polyacrylonitrile (PAN). Asphaltene, a byproduct of petroleum refining, has been considered as an appealing precursor of CNFs due to its high carbon content and low cost. This study explores the fabrication of CNFs from Alberta Oilsands Asphaltene (AOA) via electrospinning. In the first stage, this study aimed to identify viable routes for producing CNFs with as-received AOA. Based on previous studies, the effects of electrospinning parameters, polymer type, AOA/polymer ratio, dimethylformamide (DMF) and tetrahydrofuran (THF) solvent ratio, and solution concentration were systematically investigated. While PAN-AOA mixtures showed severe flocculation and poor spinnability, blends with polyethylene oxide (PEO) enabled successful fiber formation. Optimal conditions (concentration = 15 wt%, AOA: PEO = 9:1, DMF/THF = 1:1, 17 kV) yielded uniform electrospun nanofibers (ESNFs), but carbonization led to severe fiber fusion. Further thermogravimetric analysis (TGA) confirmed a loss of AOA content during electrospinning. In the next stage, nitric acid treatment was applied to AOA, producing nitric-acid-treated AOA (NT-AOA). Characterization by elemental analysis (EA), Fourier transform infrared spectroscopy (FTIR ), and nuclear magnetic resonance (NMR) confirmed chemical modifications. The addition of sodium dodecyl sulfate (SDS) further enhanced ESNF morphology by reducing surface tension. The best results were achieved with ~27 wt% solution, NT-AOA: PEO = 95:5, and 2 wt% SDS, yielding bead-free ESNFs. Carbonization produced CNFs with average diameters of ~172 nm, characterized by scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), and X-ray diffraction (XRD). Subsequent KOH activation resulted in activated CNFs (ACNFs) with a high surface area (984 m²/g) and enhanced porosity. Electrochemical testing showed a specific capacitance of 189 F/g and 84.8% retention after 5000 cycles, demonstrating the potential of NT-AOA-derived CNFs as low-cost, free-standing electrodes for supercapacitors.
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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) | 0.000 | 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".