Plasma–graphene interactions: combined effects of positive ions, vacuum-ultraviolet photons, and metastable species
Bibliographic record
Abstract
Abstract This study compares the impact of different plasma environments on the damage formation dynamics of polycrystalline monolayer graphene films on SiO 2 /Si substrates and investigates the combined effects often observed in low-pressure argon plasmas. After careful characterization of the discharge properties by Langmuir probes and optical absorption spectroscopy, three operating conditions were selected to promote graphene irradiation by either positive ions, metastable species, or vacuum-ultraviolet (VUV) photons. In all cases, hyperspectral Raman imaging of graphene reveals plasma-induced damage. In addition, defect generation is systematically slower at grain boundaries (GBs) than within the grains, a behavior ascribed to a preferential self-healing of plasma-induced defects at GBs. The evolution of selected Raman band parameters is also correlated with the energy fluence provided to the graphene lattice by very-low-energy ions. From such correlation, it is shown that the presence of VUV photons enhances the defect formation dynamics through additional energy transfer. On the other hand, the presence of metastable species first impedes the defect generation and then promotes it for higher lattice disorder. While this impediment can be linked to an enhanced defect migration and self-healing at nanocrystallite boundaries in graphene, such effect vanishes in more heavily-damaged films.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".