Role of self-healing on defects generation and nitrogen incorporation in graphene exposed to diffuse dielectric barrier discharge in N <sub>2</sub>
Bibliographic record
Abstract
Abstract Tuning the defect density and incorporation level of dopants are central challenges in the process engineering of low-dimensional materials. Although there is a surge of interest in modifying graphene by plasma treatments, the role of self-healing occurring over a specific range of plasma conditions remains undiscussed. In this work, an experimental architecture is proposed to examine the effect of self-healing on defect formation and N incorporation in graphene exposed to diffuse dielectric barrier discharge operated in N 2 at atmospheric pressure. The energy provided by plasma to carbon atoms from the graphene lattice is divided into various sub-doses with a long relaxation phase between each dose. Hyperspectral Raman spectroscopy and x-ray photoelectron spectroscopy measurements performed after plasma treatments reveal that splitting the total energy uptake results in lower defect density and higher nitrogen incorporation. Indeed, this strategy enables a reduction of the defect density by two orders of magnitude between 1 × 30 s and 5 × 6 s treatments, while linearly increasing the nitrogen incorporation. Based on these findings, possible mechanisms occurring during the plasma ON and OFF times are discussed to explain how a relaxation phase can be used to tune graphene self-healing and the N incorporation.
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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) | 0.002 | 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".