Quantum-Enhanced Second-Order Nonlinearity in Graphene: The Role of Wave Momentum and DC Biasing
Bibliographic record
Abstract
A comprehensive and rigorous analysis is presented for the study of terahertz photomixing process in a biased graphene layer, when two obliquely incident waves are used as primary excitation. The second-order nonlinearity tensor of graphene associated with the difference frequency generation (DFG) is calculated to evaluate the amount of induced terahertz current density. In this analysis, we are calculating the significant contribution of the photon drag effect to the DFG and consequently to the terahertz wave generation. We also examine the effect of DC current biasing on the DFG as the Fermi energy level of graphene changes. Our results show that the DFG in graphene can be enhanced by at least two orders of magnitude when the Fermi energy level of graphene becomes equal to the energy of incident photon. This paper provides more insight into the contributing factors in the DFG process and allows the realization of more optimal graphene-based photomixing devices.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".