Selective nitrogen doping of graphene due to preferential healing of plasma-generated defects near grain boundaries
Bibliographic record
Abstract
Abstract Hyperspectral Raman IMAging (RIMA) is used to study spatially inhomogeneous polycrystalline monolayer graphene films grown by chemical vapor deposition. Based on principal component analysis clustering, distinct regions are differentiated and probed after subsequent exposures to the late afterglow of a microwave nitrogen plasma at a reduced pressure of 6 Torr (800 Pa). The 90 × 90 µm2RIMA mapping shows differentiation between graphene domains (GDs), grain boundaries (GBs), as well as contaminants adsorbed over and under the graphene layer. Through an analysis of a few relevant band parameters, the mapping further provides a statistical assessment of damage, strain, and doping levels in plasma-treated graphene. It is found that GBs exhibit lower levels of damage and N-incorporation than GDs. The selectivity at GBs is ascribed to (i) a low migration barrier of C adatoms compared to N-adatoms and vacancies and (ii) an anisotropic transport of C adatoms along GBs, which enhances adatom-vacancy recombination at GBs. This preferential self-healing at GBs of plasma-induced damage ensures selective incorporation of N-dopants at plasma-generated defect sites within GDs. This surprising selectivity vanishes, however, as the graphene approaches an amorphous state.
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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".