A combination of plasma diagnostics and Raman spectroscopy to examine plasma-graphene interactions in low-pressure argon radiofrequency plasmas
Bibliographic record
Abstract
Graphene films were exposed to low-pressure capacitively coupled (E-mode) and inductively coupled (H-mode) argon radio frequency plasmas to investigate damage formation by very-low-energy ion irradiation. In the H-mode, plasma parameters were assessed by a Langmuir probe and plasma sampling mass spectrometry to determine the conditions of fixed ion fluence but with different average ion energies. The populations of argon metastable and resonant argon atoms were also measured by optical absorption spectroscopy to determine their contribution to the total energy flux during plasma treatment. In the H-mode, in which plasma-graphene interactions are dominated by ion irradiation effects, Raman spectroscopy reveals a significant rise in the D/G ratio and full width at half maximum of the G peak as well as the onset of graphene amorphization, even at very low ion energies (between 7 and 13 eV). In the E-mode characterized by comparable ion energy but much lower ion density, significant damage is also observed, a feature ascribed to the additional energy flux linked to the de-excitation of metastable argon species on the graphene surface.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".