High Areal Capacitance of N‐Doped Graphene Synthesized by Arc Discharge
Bibliographic record
Abstract
Abstract The lack of cost effective, industrial‐scale production methods hinders the widespread applications of graphene materials. In spite of its applicability in the mass production of graphene flakes, arc discharge has not received considerable attention because of its inability to control the synthesis and heteroatom doping. In this study, a facile approach is proposed for improving doping efficiency in N‐doped graphene synthesis through arc discharge by utilizing anodic carbon fillers. Compared to the N‐doped graphene (1–1.5% N) synthesized via the arc process according to previous literature, the resulting graphene flakes show a remarkably increased doping level (≈3.5% N) with noticeable graphitic N enrichment, which is rarely achieved by the conventional process, while simultaneously retaining high turbostratic crystallinity. The electrolyte ion storage of synthesized materials is examined in which synthesized N‐doped graphene material exhibits a remarkable area normalized capacitance of 63 µF cm−2. The surprisingly high areal capacitance, which is superior to that of most carbon materials, is attributed to the synergistic effect of extrinsic pseudocapacitance, high crystallinity, and abundance of exposed graphene edges. These results highlight the great potentials of N‐doped graphene flakes produced by arc discharge in graphene‐based supercapacitors, along with well‐studied active exfoliated graphene and reduced graphene oxide.
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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".