Treatment of graphene films in the early and late afterglows of N <sub>2</sub> plasmas: comparison of the defect generation and N-incorporation dynamics
Bibliographic record
Abstract
Abstract Graphene films grown on copper substrate by chemical vapor deposition were exposed to the flowing afterglow of a reduced-pressure N 2 plasma sustained by microwave electromagnetic fields (surface-wave plasma). Two set of conditions were examined by controlling the gas flow rate: the late afterglow (LA) characterized by a high number densities of reactive N atoms and the early afterglow (EA) in which significant populations of metastable N 2 (A) states and positive ions (N 2 + and N 4 + ) coexist with plasma-generated N atoms. LA treatments of graphene films show monotonous and steady incorporation of nitrogen atoms along with very low damage. However, given the very mild LA treatment conditions, a large part of the N atoms remains weakly bonded to the graphene surface; a feature ascribed to the plasma-induced functionalization of airborne hydrocarbon contaminants. In such conditions, graphitic inclusion of plasma-generated N atoms is limited to native defect sites. On the other hand, the presence of highly energetic species in the EA induces significant damage combined with much higher N-incorporation. Detailed Raman analysis of EA-treated samples further reveals a transition from vacancy-type defects to much larger multi-vacancies with increasing treatment time. This complete set of data indicates that through a judicious control of the populations of reactive N atoms, metastable N 2 (A) states, and positive ions (N 2 + and N 4 + ), the flowing afterglow of microwave N 2 plasmas represents a highly promising tool for precise, post-growth tuning of the defect generation and N-incorporation dynamics in graphene films.
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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".