Comparative Study of Various Types of Metal-Free N and S Co-Doped Porous Graphene for High Performance Oxygen Reduction Reaction in Alkaline Solution
Bibliographic record
Abstract
Heteroatom doping into carbon structures is an effective approach to enhance the electrochemical performance of carbon materials. In the work presented here, the electrocatalysts including: nitrogen and co-doped nitrogen and sulfur on porous graphene (PG) were synthesized by different precursors. The physico-chemical properties of the prepared samples were determined using X-ray Diffraction (XRD), Raman spectroscopy, Fourier transform infrared spectroscopy (FTIR), N2 sorption-desorption, Transmission electron microscopy (TEM), Field Emission Scanning Electron Microscopy (FESEM) and X-ray photoelectron spectroscopy (XPS). The prepared samples were further applied for oxygen reduction reaction (ORR) and the effects of pyrolysis temperature, precursor type and dose, on the prepared samples structure and their electrochemical performances were investigated. The results revealed that synergistic effect of nitrogen and sulfur co-doped on the graphene structure leads to improvement in catalytic activity and current. Furthermore, S and N co-doped graphene prepared using sulfur trioxide pyridine complex exhibited excellent methanol tolerance and long-term stability.
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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".