Heteroatom-doped electrochemically exfoliated graphene thin films: A Raman spectroscopy and density functional theory study
Bibliographic record
Abstract
This work presents a combined experimental and theoretical investigation of freestanding, heteroatom-doped electrochemically exfoliated graphene (EEG) thin films. The films were synthesized via a two-step process involving graphite intercalation and exfoliation in a sulphuric-phosphoric acid mixture, enabling in-situ doping with nitrogen (N), phosphorus (P), and sulphur (S). The exfoliated product was vacuum-filtered to produce porous films. Raman spectroscopy revealed Fermi level shifts of ~0.5 eV and heterogeneous defect distributions. Electrical conductivity was significantly enhanced (~10,000 S·m −1 ), attributed to effective heteroatom incorporation. X-ray photoelectron spectroscopy confirmed successful doping, while force-distance curve measurements showed reduced adhesion forces, indicating improved interfacial properties. Complementary density functional theory (DFT) calculations provided atomic-level insights into mono- and multi-element doping effects. N and O dopants caused localized charge redistribution with minimal lattice distortion, while P and S introduced more pronounced structural perturbations and delocalized electronic states. Co-doped models with O, N, P, and S exhibited larger Fermi level shifts (up to ~1 eV) and increased carrier densities (~1.25 × 10 14 e/cm 2 ). Bader charge analysis established a strong correlation between dopant identity, charge localization, and doping efficiency. Electrochemical investigations indicated that Fermi level shifts enhance interfacial charge transfer by lowering the potential barrier between the electrode and electrolyte. The EEG film displayed a prolonged discharge time, and a specific capacitance of 150.5 F g −1 at 1.0 A g −1 . These results provide a comprehensive approach for engineering doped EEG thin films with tailored electronic properties for supercapacitor applications.
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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 | 0.001 | 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".