Dependence of the Efficiency of the Nonlinear‐Optical Response of Materials on their Linear Permittivity and Permeability
Bibliographic record
Abstract
Abstract The dependence of the efficiency of various nonlinear‐optical processes on the background linear relative electric permittivity ϵ and magnetic permeability μ of the material is analytically and numerically investigated. The conversion efficiency of low‐order harmonic‐generation processes, as well as the increase rate of Kerr‐effect nonlinear phase shift and nonlinear losses from two‐photon absorption (TPA), are seen to increase with decreasing ϵ and/or increasing μ. The rationale and physical insights behind this nonlinear response are also discussed, particularly its enhancement in ϵ‐near‐zero (ENZ) media. This behavior is consistent with the experimental observation of intriguingly high effective nonlinear refractive indices in degenerate semiconductors such as indium tin oxide and aluminum oxide (where the nonlinearity is attributed to a modification of the energy distribution of conduction‐band electrons due to laser‐induced electron heating) at frequencies with vanishing real part of the linear permittivity. Such strong nonlinear response can pave the way for a new paradigm in nonlinear optics with much higher conversion efficiencies and therefore better miniaturization capabilities and power requirements for next‐generation integrated nanophotonics. It is concluded that the major contribution to the enhanced nonlinear response of ENZ materials arises from propagation effects, that is, the appearance of ϵ and μ in the reduced wave equation describing the interaction.
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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) | 0.000 | 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".