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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mrow> <mml:msub> <mml:mrow> <mml:mtext>N</mml:mtext> </mml:mrow> <mml:mn>2</mml:mn> </mml:msub> </mml:mrow> </mml:mrow> </mml:math> 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".