Recent Advances in Graphene-Assisted Nonlinear Optical Signal Processing
Bibliographic record
Abstract
Possessing a variety of remarkable optical, electronic, and mechanical properties, graphene has emerged as an attractive material for a myriad of optoelectronic applications. The wonderful optical properties of graphene afford multiple functions of graphene based polarizers, modulators, transistors, and photodetectors. So far, the main focus has been on graphene based photonics and optoelectronics devices. Due to the linear band structure allowing interband optical transitions at all photon energies, graphene has remarkably large third-order optical susceptibility χ(3) , which is only weakly dependent on the wavelength in the near-infrared frequency range. The graphene-assisted four-wave mixing (FWM) based wavelength conversions have been experimentally demonstrated. So, we believe that the potential applications of graphene also lie in nonlinear optical signal processing, where the combination of its unique large χ(3) nonlinearities and dispersionless over the wavelength can be fully exploited. In this review article, we give a brief overview of our recent progress in graphene-assisted nonlinear optical device and their applications, including degenerate FWM based wavelength conversion of quadrature phase-shift keying (QPSK) signal, phase conjugated wavelength conversion by degenerate FWM and transparent wavelength conversion by nondegenerate FWM, two-input and three-input high-base optical computing, and high-speed gate-tunable terahertz coherent perfect absorption (CPA) using a split-ring graphene.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".