Probing plasma-treated graphene using hyperspectral Raman
Bibliographic record
Abstract
Raman spectroscopy provides rich optical signals that can be used, after data analysis, to assess if a graphene layer is pristine, doped, damaged, functionalized, or stressed. The area being probed by a conventional Raman spectrometer is, however, limited to the size of the laser beam (∼1 µm); hence, detailed mapping of inhomogeneities in a graphene sample requires slow and sequential acquisition of a Raman spectrum at each pixel. Studies of physical and chemical processes on polycrystalline and heterogeneous graphene films require more advanced hyperspectral Raman capable of fast imaging at a high spatial resolution over hundreds of microns. Here, we compare the capacity of two different Raman imaging schemes (scanning and global) to probe graphene films modified by a low-pressure plasma treatment and present an analysis method providing assessments of the surface properties at local defects, grain boundaries, and other heterogeneities. By comparing statistically initial and plasma-treated regions of graphene, we highlight the presence of inhomogeneities after plasma treatment linked to the initial state of the graphene surface. These results provided statistical results on the correlation between the graphene initial state and the corresponding graphene-plasma interaction. This work further demonstrates the potential use of global hyperspectral Raman imaging with advanced Raman spectra analysis to study graphene physics and chemistry on a scale of hundreds of microns.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".