Bitumen and asphaltene derived nanoporous carbon and nickel oxide/carbon composites for supercapacitor electrodes
Bibliographic record
Abstract
Abstract Asphaltenes from bitumen are abundant resource to be transformed into carbon as promising supercapacitor electrodes, while there is a lack of understanding the impact from different fractions of bitumen and asphaltenes, as well as the presence of transition metals. Here, nanoporous carbon was synthesized from bitumen, hexane-insoluble asphaltenes and N , N -dimethylformamide (DMF)-fractionated asphaltenes by using Mg(OH) 2 nanoplates as the template with in-situ KOH activation, and used as an supercapacitor electrode material. All of the carbon exhibited large surface area (1500–2200 m 2 g −1 ) with a distribution of micro and mesopores except for that derived from the DMF-soluble asphaltenes. The pyrolysis of asphaltenes resulted in the formation of nickel oxide/carbon composite (NiO/C), which demonstrated high capacitance of 380 F g −1 at 1 A g −1 discharge current resulting from the pseudocapacitance of NiO and the electrochemical double layer capacitance of the carbon. The NiO/C composite obtained from the DMF-insoluble portion had low NiO content which led to lower capacitance. Meanwhile, the specific capacitance of NiO/C composite from the DMF-soluble part was lower than the unfractionated asphaltene due to the higher NiO content resulting in lower conductivity. Therefore asphaltenes derived from nickel-rich crude bitumen is suitable for the synthesis of nanoporous NiO/C composite material with high capacitance.
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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 | 0.001 | 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) | 0.001 | 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".