Enhanced Raman scattering in graphene by plasmonic resonant Stokes emission
Bibliographic record
Abstract
Abstract Exploiting surface plasmon polaritons to enhance interactions between graphene and light has recently attracted much interest. In particular, nonlinear optical processes in graphene can be dramatically enhanced and controlled by plasmonic nanostructures. This work demonstrates Raman scattering enhancement in graphene based on plasmonic resonant enhancement of the Stokes emission, and compares this mechanism with the conventional Raman enhancement by resonant pump absorption. Arrays of optical nanoantennas with different resonant frequency are utilized to independently identify the effects of each mechanism on Raman scattering in graphene via the measured enhancement factor and its spectral linewidth. We demonstrate that, while both mechanisms offer large enhancement factors (scattering cross‐section gains of 160 and 20 for individual nanoantennas, respectively), they affect the graphene Raman spectrum quite differently. Our results provide a benchmark to assess and quantify the role and merit of each mechanism in surface‐plasmon‐mediated Raman scattering in graphene, and may be employed for design and realization of a variety of graphene optoelectronic devices involving nonlinear optical processes.
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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) | 0.001 | 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".