Nitrogen-Doped Graphene Materials with High Electrical Conductivity Produced by Electrochemical Exfoliation of Graphite Foil
Bibliographic record
Abstract
Nitrogen-doped graphene-based materials are of utmost importance in sensing and energy conversion devices due to their unique physicochemical properties. However, the presence of defects such as pyrrolic nitrogen and oxygenated functional groups reduces their electrical conductivity. Herein, a two-step approach based on the electrochemical exfoliation of graphite foils in aqueous mixed electrolytes followed by thermal reduction at 900 °C is used to prepare high-quality few layers of N-doped graphene-based materials. The exfoliations were conducted in 0.1 M (NH4)2SO4 or H2SO4 and HNO3 (5 mM or 0.1 M) electrolytes mixtures and the HNO3 vol% varied. Chemical analysis demonstrated that the as-prepared graphene oxides contain nitro and amine groups. Thermal reduction is needed for substitutional N-doping. Nitrogen and oxygen surface concentrations vary between 0.23–0.96% and 3–8%, respectively. Exfoliation in (NH4)2SO4 and/or 5 mM HNO3 favors the formation of pyridinic-N (10–40% of the total N), whereas 1 M HNO3 favors the formation of graphitic-N (≈60%). The electrical conductivity ranges between 166–2705 Scm−1. Raman spectroscopy revealed a low density of defects (ID/IG ratio between 0.1 and 0.7) and that most samples are composed of mono-to-bilayer graphene-based materials (IG/I2D integrated intensities ratio). Structural and compositional stability of selected samples after storage in air for three months is demonstrated. These results confirm the high quality of the synthesized undoped and N-doped graphene-type materials.
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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".