Experimental Characterization of the Ultrafast, Tunable and Broadband Optical Kerr Nonlinearity in Graphene
Bibliographic record
Abstract
Abstract Graphene’s giant nonlinear optical response along with its integrability has made it a vaunted material for on-chip photonics. Despite a multitude of studies confirming its strong nonlinearity, there is a lack of reports examining the fundamental processes that govern the response. Addressing this gap in knowledge we analyse the role of experimental parameters by systematically measuring the near-infrared spectral dependence, the sub-picosecond temporal evolution and pulse-width dependence of the effective Kerr coefficient ( n 2 , eff ) of graphene in hundreds of femtosecond regime. The spectral dependence measured using the Z-scan technique is corroborated by a density matrix quantum theory formulation to extract a n 2 , eff ∝ λ 2 dependence. The temporal evolution obtained using the time-resolved Z-scan measurement shows the nonlinearity peaking at zero delay time and relaxing on a time-scale of carrier relaxation. The dependence of the n 2 , eff on pulse duration is obtained by expanding the input pulse using a prism-pair set-up. Our results provide an avenue for controllable tunability of the nonlinear response in graphene, which is limited in silicon photonics.
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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".