Nitrogen incorporation in monolayer graphene films by atmospheric pressure Townsend dielectric barrier discharge in N<sub>2</sub>
Bibliographic record
Abstract
Abstract Graphene films grown by chemical vapor deposition and transferred on SiO2/Si substrates were treated by a low-frequency dielectric barrier discharge (DBD) operated in N2 at atmospheric pressure. The discharge conditions were carefully chosen to obtain a weakly-ionized Townsend discharge operated in a diffuse regime and characterized by a neutral gas temperature of 300 K. In such conditions, plasma–graphene interactions are dominated by plasma-generated N atoms and N2(A) metastable species, with lower contributions from irradiation by positive ions ( N 2 + and N 4 + ) and electrons. Defect generation and N incorporation in graphene films were studied using hyperspectral Raman imaging and x-ray photoelectron spectroscopy. Progressive rises in defect concentration and N incorporation were observed with plasma treatment time, with graphene amorphization and nitrogen-to-carbon ratios ∼7% after 60 s. Over the range of experimental conditions investigated, the rate of nitrogen incorporation (mostly pyridine, pyrrole and graphitic) is limited by defect generation in the graphene lattice and not by adsorption and surface diffusion of nitrogen atoms towards defect sites. This work opens a new path to produce large-scale N-graphene by atmospheric-pressure plasma treatments.
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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".