Coating of Low-Cost Asphaltenes-Derived Carbon Fibers with V<sub>2</sub>O<sub>5</sub> for Supercapacitor Application
Bibliographic record
Abstract
V 2 O 5 -coated, low-cost carbon fibers were produced using asphaltenes and V 2 O 3 as precursors, both industrial byproducts. Asphaltenes can be used without any further treatment to fabricate low-cost carbon fibers. The carbon fibers were coated with V 2 O 5 using a straightforward two-step coating procedure, that is, surface etching of fibers with NH 4 OH, followed by dipping into a V 2 O 3 solution. This facile process represents a time-saving and low-cost alternative to other metal oxide coating techniques, such as atomic layer deposition, to create fiber-based electrode materials. After annealing, the fibers were homogeneously coated with V 2 O 5 and exhibited porosity. Annealing time and temperature influenced the porosity and structure of the fiber material. The highest surface area (440 m 2 g –1 ) was obtained at the highest annealing temperature. The coated fibers show both micropores and mesopores. The different fiber samples were then utilized as supercapacitors using a 1 M Li 2 SO 4 electrolyte. Specific capacitances of up to 125 F g –1 at a rate of 0.25 A g –1 were achieved. Long-term cycling tests showed a capacitance retention of 89% after 10,000 cycles. A strong surface area dependence for the capacitance and increased pseudocapacitance for higher annealing temperatures are demonstrated.
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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".